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Dorsal and ventral vision; Object representations; Dopamine as reward;
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1506
Deep learning architectures
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Chen, Y.S.[Yu-Shi],
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Automatic Design of Convolutional Neural Network for Hyperspectral
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IEEE DOI
1909
Feature extraction, Deep learning, Hyperspectral imaging,
Convolution, Training, Convolutional neural network (CNN),
neural architecture search (NAS)
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Jaafra, Y.[Yesmina],
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1909
Reinforcement learning, Convolutional neural networks,
Neural Architecture Search, AutoML
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Sun, Y.,
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Automatically Designing CNN Architectures Using the Genetic Algorithm
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IEEE DOI
2008
Tuning, Genetic algorithms,
Evolutionary computation, Manuals, Genetics, Evolution (biology),
neural-network architecture optimization
BibRef
Dong, H.,
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Zhang, L.,
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Automatic Design of CNNs via Differentiable Neural Architecture
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GeoRS(58), No. 9, September 2020, pp. 6362-6375.
IEEE DOI
2008
Personal digital assistants,
Deep learning, Search problems, Neural networks,
polarimetric synthetic aperture radar (PolSAR) classification
BibRef
Liu, J.H.[Jia-Heng],
Zhou, S.F.[Shun-Feng],
Wu, Y.C.[Yi-Chao],
Chen, K.[Ken],
Ouyang, W.L.[Wan-Li],
Xu, D.[Dong],
Block Proposal Neural Architecture Search,
IP(30), 2021, pp. 15-25.
IEEE DOI
2011
Proposals, Task analysis, DNA, Convolution,
Network architecture, Evolutionary computation,
image classification
BibRef
Jing, W.P.[Wei-Peng],
Ren, Q.L.[Quan-Lin],
Zhou, J.[Jun],
Song, H.B.[Hou-Bing],
AutoRSISC: Automatic design of neural architecture for remote sensing
image scene classification,
PRL(140), 2020, pp. 186-192.
Elsevier DOI
2012
Deep learning, High resolution remote sensing,
Network architecture search (NAS), Image classification
BibRef
Nakai, K.[Kohei],
Matsubara, T.[Takashi],
Uehara, K.[Kuniaki],
Neural Architecture Search for Convolutional Neural Networks with
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IEICE(E104-D), No. 2, February 2021, pp. 312-321.
WWW Link.
2102
BibRef
Yu, Q.[Qian],
Song, J.F.[Ji-Fei],
Song, Y.Z.[Yi-Zhe],
Chen, H.L.[Han-Lin],
Zhuo, L.[Li'an],
Zhang, B.C.[Bao-Chang],
Zheng, X.W.[Xia-Wu],
Liu, J.Z.[Jian-Zhuang],
Ji, R.R.[Rong-Rong],
Doermann, D.[David],
Guo, G.D.[Guo-Dong],
Binarized Neural Architecture Search for Efficient Object Recognition,
IJCV(129), No. 2, February 2021, pp. 501-516.
Springer DOI
2102
BibRef
Zhao, J.[Junhe],
Xu, S.[Sheng],
Zhang, B.C.[Bao-Chang],
Gu, J.X.[Jia-Xin],
Doermann, D.[David],
Guo, G.D.[Guo-Dong],
Towards Compact 1-bit CNNs via Bayesian Learning,
IJCV(130), No. 2, February 2022, pp. 201-225.
Springer DOI
2202
BibRef
Wang, J.J.[Jun-Jue],
Zhong, Y.F.[Yan-Fei],
Zheng, Z.[Zhuo],
Ma, A.L.[Ai-Long],
Zhan, L.P.[Liang-Pei],
RSNet: The Search for Remote Sensing Deep Neural Networks in
Recognition Tasks,
GeoRS(59), No. 3, March 2021, pp. 2520-2534.
IEEE DOI
2103
Task analysis, Image recognition, Remote sensing,
Neural networks, Feature extraction,
search for convolutional neural networks (CNNs)
BibRef
Chen, X.[Xin],
Xie, L.X.[Ling-Xi],
Wu, J.[Jun],
Tian, Q.[Qi],
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild,
IJCV(129), No. 3, March 2021, pp. 638-655.
Springer DOI
2103
Neural Architecture Search.
BibRef
Chen, Z.S.[Zheng-Su],
Xie, L.X.[Ling-Xi],
Niu, J.W.[Jian-Wei],
Liu, X.F.[Xue-Feng],
Wei, L.H.[Long-Hui],
Tian, Q.[Qi],
Network Adjustment: Channel and Block Search Guided by Resource
Utilization Ratio,
IJCV(130), No. 3, March 2022, pp. 820-835.
Springer DOI
2203
BibRef
Earlier: A1, A3, A2, A4, A5, A6:
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio,
CVPR20(10655-10664)
IEEE DOI
2008
Training, Neural networks,
Channel estimation, Pipelines, Standards
BibRef
Liu, X.B.[Xiao-Bo],
Zhang, C.C.[Chao-Chao],
Cai, Z.H.[Zhi-Hua],
Yang, J.F.[Jian-Feng],
Zhou, Z.L.[Zhi-Lang],
Gong, X.[Xin],
Continuous Particle Swarm Optimization-Based Deep Learning
Architecture Search for Hyperspectral Image Classification,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Liu, H.Y.[Hong-Ying],
Xu, D.R.[De-Rong],
Zhu, T.W.[Tian-Wen],
Shang, F.H.[Fan-Hua],
Liu, Y.Y.[Yuan-Yuan],
Lu, J.H.[Jian-Hua],
Yang, R.[Ri],
Graph Convolutional Networks by Architecture Search for PolSAR Image
Classification,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Chen, Z.Q.[Zhi-Qiang],
Xu, T.B.[Ting-Bing],
Liao, W.J.[Wei-Jian],
Li, Z.C.[Zheng-Cheng],
Li, J.P.[Jin-Peng],
Liu, C.L.[Cheng-Lin],
He, H.G.[Hui-Guang],
SNAP: Shaping neural architectures progressively via information
density criterion,
PR(116), 2021, pp. 107923.
Elsevier DOI
2106
Auto-generated neural architectures, Information density,
Greedy strategy, Progressively, Efficient and adaptive
BibRef
Khatib, R.[Rajaei],
Simon, D.[Dror],
Elad, M.[Michael],
Learned Greedy Method (LGM):
A novel neural architecture for sparse coding and beyond,
JVCIR(77), 2021, pp. 103095.
Elsevier DOI
2106
Sparse representation, Orthogonal Matching Pursuit,
Unfolding pursuit algorithms, Deraining
BibRef
Peng, C.[Cheng],
Li, Y.Y.[Yang-Yang],
Jiao, L.C.[Li-Cheng],
Shang, R.H.[Rong-Hua],
Efficient Convolutional Neural Architecture Search for Remote Sensing
Image Scene Classification,
GeoRS(59), No. 7, July 2021, pp. 6092-6105.
IEEE DOI
2106
Remote sensing, Task analysis,
Feature extraction, Data models, Machine learning, Semantics,
scene classification
BibRef
Zhang, B.F.[Bao Feng],
Zhou, G.Q.[Guo Qiang],
Control the number of skip-connects to improve robustness of the NAS
algorithm,
IET-CV(15), No. 5, 2021, pp. 356-365.
DOI Link
2107
neural architecture search
BibRef
Zhang, X.B.[Xin-Bang],
Huang, Z.[Zehao],
Wang, N.Y.[Nai-Yan],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
You Only Search Once: Single Shot Neural Architecture Search via
Direct Sparse Optimization,
PAMI(43), No. 9, September 2021, pp. 2891-2904.
IEEE DOI
2108
Optimization,
Learning (artificial intelligence), Task analysis, Acceleration,
sparse optimization
BibRef
Zhang, X.B.[Xin-Bang],
Chang, J.L.[Jian-Long],
Guo, Y.[Yiwen],
Meng, G.F.[Gao-Feng],
Xiang, S.M.[Shi-Ming],
Lin, Z.C.[Zhou-Chen],
Pan, C.H.[Chun-Hong],
DATA: Differentiable ArchiTecture Approximation With Distribution
Guided Sampling,
PAMI(43), No. 9, September 2021, pp. 2905-2920.
IEEE DOI
2108
Search problems, Optimization,
Task analysis, Bridges, Binary codes, Estimation,
distribution guided sampling
BibRef
Zheng, X.[Xiawu],
Ji, R.R.[Rong-Rong],
Chen, Y.H.[Yu-Hang],
Wang, Q.[Qiang],
Zhang, B.C.[Bao-Chang],
Chen, J.[Jie],
Ye, Q.X.[Qi-Xiang],
Huang, F.Y.[Fei-Yue],
Tian, Y.H.[Yong-Hong],
MIGO-NAS: Towards Fast and Generalizable Neural Architecture Search,
PAMI(43), No. 9, September 2021, pp. 2936-2952.
IEEE DOI
2108
Training, Dynamic programming,
Graphics processing units, Task analysis,
dynamic programming
BibRef
Xu, Y.H.[Yu-Hui],
Xie, L.X.[Ling-Xi],
Dai, W.R.[Wen-Rui],
Zhang, X.P.[Xiao-Peng],
Chen, X.[Xin],
Qi, G.J.[Guo-Jun],
Xiong, H.K.[Hong-Kai],
Tian, Q.[Qi],
Partially-Connected Neural Architecture Search for Reduced
Computational Redundancy,
PAMI(43), No. 9, September 2021, pp. 2953-2970.
IEEE DOI
2108
Redundancy, Network architecture,
Stability analysis, Microprocessors, Space exploration,
normalization
BibRef
Tang, Y.H.[Ye-Hui],
Wang, Y.H.[Yun-He],
Xu, Y.X.[Yi-Xing],
Chen, H.T.[Han-Ting],
Shi, B.X.[Bo-Xin],
Xu, C.[Chao],
Xu, C.J.[Chun-Jing],
Tian, Q.[Qi],
Xu, C.[Chang],
A Semi-Supervised Assessor of Neural Architectures,
CVPR20(1807-1816)
IEEE DOI
2008
Training, Task analysis, Microprocessors,
Optimization, Neural networks, Feature extraction
BibRef
Lu, Z.C.[Zhi-Chao],
Sreekumar, G.[Gautam],
Goodman, E.[Erik],
Banzhaf, W.[Wolfgang],
Deb, K.[Kalyanmoy],
Boddeti, V.N.[Vishnu Naresh],
Neural Architecture Transfer,
PAMI(43), No. 9, September 2021, pp. 2971-2989.
IEEE DOI
2108
BibRef
Earlier: A1, A5, A3, A4, A6, Only:
Nsganetv2: Evolutionary Multi-objective Surrogate-assisted Neural
Architecture Search,
ECCV20(I:35-51).
Springer DOI
2011
Task analysis, Search problems,
Predictive models, Computational modeling, Training,
evolutionary algorithms
BibRef
Fang, J.M.[Jie-Min],
Sun, Y.Z.[Yu-Zhu],
Zhang, Q.[Qian],
Peng, K.J.[Kang-Jian],
Li, Y.[Yuan],
Liu, W.Y.[Wen-Yu],
Wang, X.G.[Xing-Gang],
FNA++: Fast Network Adaptation via Parameter Remapping and
Architecture Search,
PAMI(43), No. 9, September 2021, pp. 2990-3004.
IEEE DOI
2108
Task analysis, Object detection, Semantics,
Image segmentation, Search problems, Pose estimation,
neural architecture search
BibRef
Chen, Y.K.[Yu-Kang],
Meng, G.F.[Gao-Feng],
Zhang, Q.[Qian],
Xiang, S.M.[Shi-Ming],
Huang, C.[Chang],
Mu, L.[Lisen],
Wang, X.G.[Xing-Gang],
RENAS: Reinforced Evolutionary Neural Architecture Search,
CVPR19(4782-4791).
IEEE DOI
2002
BibRef
Tian, Y.J.[Yun-Jie],
Liu, C.[Chang],
Xie, L.X.[Ling-Xi],
jiao, J.B.[Jian-Bin],
Ye, Q.X.[Qi-Xiang],
Discretization-aware architecture search,
PR(120), 2021, pp. 108186.
Elsevier DOI
2109
Neural architecture search, Weight-sharing,
Discretization-aware, Imbalanced network configuration
BibRef
Lukasik, J.[Jovita],
Friede, D.[David],
Stuckenschmidt, H.[Heiner],
Keuper, M.[Margret],
Neural Architecture Performance Prediction Using Graph Neural Networks,
GCPR20(188-201).
Springer DOI
2110
BibRef
Wang, N.[Ning],
Gao, Y.[Yang],
Chen, H.[Hao],
Wang, P.[Peng],
Tian, Z.[Zhi],
Shen, C.H.[Chun-Hua],
Zhang, Y.N.[Yan-Ning],
NAS-FCOS: Efficient Search for Object Detection Architectures,
IJCV(129), No. 12, December 2021, pp. 3299-3312.
Springer DOI
2111
BibRef
Earlier:
NAS-FCOS: Fast Neural Architecture Search for Object Detection,
CVPR20(11940-11948)
IEEE DOI
2008
Object detection, Search problems,
Task analysis, Decoding, Feature extraction, Detectors
BibRef
Wang, Z.Y.[Zu-Yuan],
Remote Sensing Scene Classification via Multi-Branch Local Attention
Network,
IP(31), 2022, pp. 99-109.
IEEE DOI
2112
Different classes aren't that different.
Remote sensing, Feature extraction,
Convolutional neural networks, Image color analysis,
attention mechanism
BibRef
Chen, J.[Jie],
Huang, H.Z.[Hao-Zhe],
Peng, J.[Jian],
Zhu, J.W.[Jia-Wei],
Chen, L.[Li],
Tao, C.[Chao],
Li, H.F.[Hai-Feng],
Contextual Information-Preserved Architecture Learning for
Remote-Sensing Scene Classification,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI
2112
Remote sensing, Task analysis, Convolution,
Architecture, Semantics, Space exploration, scene classification
BibRef
Zhang, Z.[Zhen],
Liu, S.H.[Shang-Hao],
Zhang, Y.[Yang],
Chen, W.[Wenbo],
RS-DARTS: A Convolutional Neural Architecture Search for Remote
Sensing Image Scene Classification,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Li, G.[Gen],
Gu, Y.T.[Yuan-Tao],
Ding, J.[Jie],
L_1 Regularization in Two-Layer Neural Networks,
SPLetters(29), 2022, pp. 135-139.
IEEE DOI
2202
Biological neural networks, Complexity theory, Neurons, Training,
Approximation error, Convergence, Computational modeling, regularization
BibRef
Shi, Z.L.[Zeng-Lin],
Mettes, P.S.[Pascal S.],
Maji, S.[Subhransu],
Snoek, C.G.M.[Cees G. M.],
On Measuring and Controlling the Spectral Bias of the Deep Image Prior,
IJCV(130), No. 1, January 2022, pp. 885-908.
Springer DOI
2204
Code, Spectal Bias.
WWW Link.
BibRef
Guo, Q.[Qingbei],
Wu, X.J.[Xiao-Jun],
Kittler, J.V.[Josef V.],
Feng, Z.Q.[Zhi-Quan],
Differentiable neural architecture learning for efficient neural
networks,
PR(126), 2022, pp. 108448.
Elsevier DOI
2204
Deep neural network, Convolutional neural network,
Neural architecture search, Automated machine learning
BibRef
Dong, N.Q.[Nan-Qing],
Kampffmeyer, M.[Michael],
Voiculescu, I.[Irina],
Xing, E.[Eric],
Negational symmetry of quantum neural networks for binary pattern
classification,
PR(129), 2022, pp. 108750.
Elsevier DOI
2206
Deep learning, Quantum machine learning,
Binary pattern classification, Representation learning, Symmetry
BibRef
Dong, X.[Xuanyi],
Liu, L.[Lu],
Musial, K.[Katarzyna],
Gabrys, B.[Bogdan],
NATS-Bench:
Benchmarking NAS Algorithms for Architecture Topology and Size,
PAMI(44), No. 7, July 2022, pp. 3634-3646.
IEEE DOI
2206
Topology, Microprocessors,
Benchmark testing, Training, Search problems, Deep learning,
deep learning
BibRef
Hu, Y.F.[Yu-Fei],
Belkhir, N.[Nacim],
Angulo, J.[Jesus],
Yao, A.[Angela],
Franchi, G.[Gianni],
Learning deep morphological networks with neural architecture search,
PR(131), 2022, pp. 108893.
Elsevier DOI
2208
Mathematical morphology, Deep learning, Architecture search,
Edge detection, Semantic segmentation
BibRef
Ren, X.H.[Xu-Hong],
Chen, J.[Jianlang],
Juefei-Xu, F.[Felix],
Xue, W.L.[Wan-Li],
Guo, Q.[Qing],
Ma, L.[Lei],
Zhao, J.J.[Jian-Jun],
Chen, S.Y.[Sheng-Yong],
DARTSRepair: Core-failure-set guided DARTS for network robustness to
common corruptions,
PR(131), 2022, pp. 108864.
Elsevier DOI
2208
Network architecture search, Core-failure-set selection,
Robustness enhancement, Differentiable architecture search
BibRef
Liu, J.Y.[Jin-Yuan],
Wu, Y.H.[Yu-Hui],
Wu, G.Y.[Guan-Yao],
Liu, R.S.[Ri-Sheng],
Fan, X.[Xin],
Learn to Search a Lightweight Architecture for Target-Aware Infrared
and Visible Image Fusion,
SPLetters(29), 2022, pp. 1614-1618.
IEEE DOI
2208
Training, Image fusion, Task analysis,
Search problems, Feature extraction, Fuses, Deep learning,
neural architecture search
BibRef
Wang, L.[Linnan],
Xie, S.[Saining],
Li, T.[Teng],
Fonseca, R.[Rodrigo],
Tian, Y.D.[Yuan-Dong],
Sample-Efficient Neural Architecture Search by Learning Actions for
Monte Carlo Tree Search,
PAMI(44), No. 9, September 2022, pp. 5503-5515.
IEEE DOI
2208
Vegetation, Optimization, Measurement, Bayes methods, Task analysis,
Search problems, Monte Carlo methods, Neural architecture search,
Monte Carlo tree search
BibRef
Wang, Y.[Yu],
Li, Y.S.[Yan-Sheng],
Chen, W.[Wei],
Li, Y.Z.[Yun-Zhou],
Dang, B.[Bo],
DNAS: Decoupling Neural Architecture Search for High-Resolution
Remote Sensing Image Semantic Segmentation,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Tian, Y.[Yuesong],
Shen, L.[Li],
Shen, L.[Li],
Su, G.[Guinan],
Li, Z.F.[Zhi-Feng],
Liu, W.[Wei],
AlphaGAN: Fully Differentiable Architecture Search for Generative
Adversarial Networks,
PAMI(44), No. 10, October 2022, pp. 6752-6766.
IEEE DOI
2209
Generators, Search problems,
Generative adversarial networks, Training, Nash equilibrium,
generative models
BibRef
Tong, L.Y.[Lyu-Yang],
Du, B.[Bo],
Neural architecture search via reference point based multi-objective
evolutionary algorithm,
PR(132), 2022, pp. 108962.
Elsevier DOI
2209
Neural architecture search,
Multi-objective evolutionary algorithm, The image classification
BibRef
Guo, Y.[Yong],
Zheng, Y.[Yin],
Tan, M.K.[Ming-Kui],
Chen, Q.[Qi],
Li, Z.P.[Zhi-Peng],
Chen, J.[Jian],
Zhao, P.[Peilin],
Huang, J.Z.[Jun-Zhou],
Towards Accurate and Compact Architectures via Neural Architecture
Transformer,
PAMI(44), No. 10, October 2022, pp. 6501-6516.
IEEE DOI
2209
Optimization, Computational efficiency,
Convolution, Computational modeling, Kernel, Microprocessors,
operation transition
BibRef
Shen, H.[Hao],
Zhao, Z.Q.[Zhong-Qiu],
Liao, W.[Wenrui],
Tian, W.D.[Wei-Dong],
Huang, D.S.[De-Shuang],
Joint operation and attention block search for lightweight image
restoration,
PR(132), 2022, pp. 108909.
Elsevier DOI
2209
Image restoration, Neural architecture search, Attention mechanism
BibRef
Cao, H.M.[Hui-Min],
Luo, X.B.[Xiao-Bo],
Peng, Y.D.[Yi-Dong],
Xie, T.S.[Tian-Shou],
MANet: A Network Architecture for Remote Sensing Spatiotemporal
Fusion Based on Multiscale and Attention Mechanisms,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Yu, K.C.[Kai-Cheng],
Ranftl, R.[René],
Salzmann, M.[Mathieu],
An Analysis of Super-Net Heuristics in Weight-Sharing NAS,
PAMI(44), No. 11, November 2022, pp. 8110-8124.
IEEE DOI
2210
BibRef
Earlier:
Landmark Regularization: Ranking Guided Super-Net Training in Neural
Architecture Search,
CVPR21(13718-13727)
IEEE DOI
2111
Training, Protocols, Task analysis, Measurement, Benchmark testing,
Encoding, AutoML, neural architecture search, weight-sharing, super-net.
Correlation, Limiting, Computational modeling, Hardware
BibRef
Liu, Z.J.[Zhi-Jian],
Tang, H.T.[Hao-Tian],
Zhao, S.Y.[Sheng-Yu],
Shao, K.[Kevin],
Han, S.[Song],
PVNAS: 3D Neural Architecture Search With Point-Voxel Convolution,
PAMI(44), No. 11, November 2022, pp. 8552-8568.
IEEE DOI
2210
Convolution, Solid modeling, Random access memory,
Computational modeling, Memory management, Neural networks,
autonomous driving
BibRef
Tang, H.T.[Hao-Tian],
Liu, Z.J.[Zhi-Jian],
Zhao, S.Y.[Sheng-Yu],
Lin, Y.J.[Yu-Jun],
Lin, J.[Ji],
Wang, H.R.[Han-Rui],
Han, S.[Song],
Searching Efficient 3d Architectures with Sparse Point-voxel
Convolution,
ECCV20(XXVIII:685-702).
Springer DOI
2011
BibRef
Li, H.F.[Hai-Feng],
Xu, C.[Cong],
Ma, L.[Lin],
Bo, H.J.[Hong-Jian],
Zhang, D.[David],
MODENN: A Shallow Broad Neural Network Model Based on Multi-Order
Descartes Expansion,
PAMI(44), No. 12, December 2022, pp. 9417-9433.
IEEE DOI
2212
Neurons, Biological neural networks, Task analysis, Brain modeling,
Parallel processing, Information processing, Training,
parallel computing
BibRef
Yu, H.Y.[Hong-Yuan],
Peng, H.[Houwen],
Huang, Y.[Yan],
Fu, J.L.[Jian-Long],
Du, H.[Hao],
Wang, L.[Liang],
Ling, H.B.[Hai-Bin],
Cyclic Differentiable Architecture Search,
PAMI(45), No. 1, January 2023, pp. 211-228.
IEEE DOI
2212
Computer architecture, Optimization, Search problems,
Task analysis, Training, Microprocessors, Object detection, Cyclic,
unified framework
BibRef
Gudzius, P.[Povilas],
Kurasova, O.[Olga],
Darulis, V.[Vytenis],
Filatovas, E.[Ernestas],
AutoML-Based Neural Architecture Search for Object Recognition in
Satellite Imagery,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Zhang, Y.Q.[Yong-Qi],
Yao, Q.M.[Quan-Ming],
Kwok, J.T.[James T.],
Bilinear Scoring Function Search for Knowledge Graph Learning,
PAMI(45), No. 2, February 2023, pp. 1458-1473.
IEEE DOI
2301
Task analysis, Artificial neural networks, Training,
Machine learning, Evolutionary computation, neural architecture search
BibRef
Fu, S.[Siming],
Chu, H.[Huanpeng],
Yu, L.[Lu],
Peng, B.[Bo],
Li, Z.[Zheyang],
Tan, W.M.[Wen-Ming],
Hu, H.J.[Hao-Ji],
AuxBranch: Binarization residual-aware network design via auxiliary
branch search,
PR(136), 2023, pp. 109263.
Elsevier DOI
2301
Binary neural network, Binarization residual,
Performance estimation indicator, Neural architecture search
BibRef
Wang, W.[Wenna],
Zhang, X.[Xiuwei],
Cui, H.[Hengfei],
Yin, H.L.[Han-Lin],
Zhang, Y.N.[Yan-Nnig],
FP-DARTS: Fast parallel differentiable neural architecture search for
image classification,
PR(136), 2023, pp. 109193.
Elsevier DOI
2301
Neural architecture search, Computing overheads,
Operator sub-sets, Two-parallel-path, Binary gate, Sigmoid function
BibRef
Li, W.[Wei],
Gong, S.G.[Shao-Gang],
Zhu, X.T.[Xia-Tian],
Neural operator search,
PR(136), 2023, pp. 109215.
Elsevier DOI
2301
Neural architecture search, Search space,
Self-calibration operations, Dynamic convolution, Knowledge distillation
BibRef
Ao, S.[Sheng],
Guo, Y.L.[Yu-Lan],
Hu, Q.Y.[Qing-Yong],
Yang, B.[Bo],
Markham, A.[Andrew],
Chen, Z.P.[Zeng-Ping],
You Only Train Once:
Learning General and Distinctive 3D Local Descriptors,
PAMI(45), No. 3, March 2023, pp. 3949-3967.
IEEE DOI
2302
Feature extraction, Point cloud compression, Histograms, Shape, Transformers,
Task analysis, Cross-dataset generalization, rotation invariance
BibRef
Chun, I.Y.[Il Yong],
Huang, Z.Y.[Zheng-Yu],
Lim, H.[Hongki],
Fessler, J.A.[Jeffrey A.],
Momentum-Net: Fast and Convergent Iterative Neural Network for
Inverse Problems,
PAMI(45), No. 4, April 2023, pp. 4915-4931.
IEEE DOI
2303
Convergence, Artificial neural networks, Imaging, Acceleration,
Image reconstruction, Optimization, Extrapolation,
X-ray computational tomography
BibRef
Zheng, X.[Xiawu],
Yang, C.Y.[Chen-Yi],
Zhang, S.[Shaokun],
Wang, Y.[Yan],
Zhang, B.C.[Bao-Chang],
Wu, Y.J.[Yong-Jian],
Wu, Y.S.[Yun-Sheng],
Shao, L.[Ling],
Ji, R.R.[Rong-Rong],
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution
Pruning,
IJCV(131), No. 5, May 2023, pp. 1234-1249.
Springer DOI
2305
BibRef
Ao, L.[Lei],
Feng, K.[Kaiyuan],
Sheng, K.[Kai],
Zhao, H.Y.[Hong-Yu],
He, X.[Xin],
Chen, Z.[Zigang],
TPENAS: A Two-Phase Evolutionary Neural Architecture Search for
Remote Sensing Image Classification,
RS(15), No. 8, 2023, pp. 2212.
DOI Link
2305
BibRef
Li, Y.X.[Yan-Xi],
Dong, M.J.[Min-Jing],
Wang, Y.H.[Yun-He],
Xu, C.[Chang],
Neural Architecture Search via Proxy Validation,
PAMI(45), No. 6, June 2023, pp. 7595-7610.
IEEE DOI
2305
Optimization, Training, Search problems, Graphics processing units,
Costs, Predictive models, Neural architecture search,
deep neural architecture
BibRef
Ning, X.F.[Xue-Fei],
Zheng, Y.[Yin],
Zhou, Z.X.[Zi-Xuan],
Zhao, T.C.[Tian-Chen],
Yang, H.Z.[Hua-Zhong],
Wang, Y.[Yu],
A Generic Graph-Based Neural Architecture Encoding Scheme With
Multifaceted Information,
PAMI(45), No. 7, July 2023, pp. 7955-7969.
IEEE DOI
2306
BibRef
Earlier: A1, A2, A4, A6, A5, Only:
A Generic Graph-based Neural Architecture Encoding Scheme for
Predictor-based NAS,
ECCV20(XIII:189-204).
Springer DOI
2011
BibRef
Su, X.[Xiu],
You, S.[Shan],
Xie, J.[Jiyang],
Wang, F.[Fei],
Qian, C.[Chen],
Zhang, C.S.[Chang-Shui],
Xu, C.[Chang],
Searching for Network Width With Bilaterally Coupled Network,
PAMI(45), No. 7, July 2023, pp. 8936-8953.
IEEE DOI
2306
BibRef
Earlier: A1, A2, A4, A5, A6, A7, Only:
BCNet: Searching for Network Width with Bilaterally Coupled Network,
CVPR21(2175-2184)
IEEE DOI
2111
Training, Hardware, Benchmark testing, Search methods,
Neural networks, Convolutional neural networks, Sociology,
stochastic complementary strategy.
Refining, Stochastic processes, Sampling methods
BibRef
Huang, H.[Han],
Shen, L.[Li],
He, C.Y.[Chao-Yang],
Dong, W.S.[Wei-Sheng],
Liu, W.[Wei],
Differentiable Neural Architecture Search for Extremely Lightweight
Image Super-Resolution,
CirSysVideo(33), No. 6, June 2023, pp. 2672-2682.
IEEE DOI
2306
Task analysis, Convolution, Superresolution,
Computational modeling, Search problems, Reinforcement learning,
lightweight model design
BibRef
Mohan, R.[Rohit],
Elsken, T.[Thomas],
Zela, A.[Arberf],
Metzen, J.H.[Jan Hendrik],
Staffler, B.[Benedikt],
Brox, T.[Thomas],
Valada, A.[Abhinav],
Hutter, F.[Frank],
Neural Architecture Search for Dense Prediction Tasks in Computer
Vision,
IJCV(131), No. 7, July 2023, pp. 1784-1807.
Springer DOI
2307
BibRef
Kang, X.T.[Xia-Tao],
Li, P.[Ping],
Yao, J.Y.[Jia-Yi],
Li, C.X.[Cheng-Xi],
Neural Network Panning: Screening the Optimal Sparse Network Before
Training,
ACCV22(I:602-617).
Springer DOI
2307
BibRef
Dou, Z.[Ziwen],
Ye, D.[Dong],
Wang, B.[Boya],
AutoSegEdge: Searching for the edge device real-time semantic
segmentation based on multi-task learning,
IVC(136), 2023, pp. 104719.
Elsevier DOI
2308
Semantic segmentation, Multi-task-learning,
Hardware-aware neural architecture search, Edge, Real-time
BibRef
Xu, P.[Peng],
Wang, K.[Ke],
Hassan, M.M.[Mohammad Mehedi],
Chen, C.M.[Chien-Ming],
Lin, W.G.[Wei-Guo],
Hassan, M.R.[Md. Rafiul],
Fortino, G.[Giancarlo],
Adversarial Robustness in Graph-Based Neural Architecture Search for
Edge AI Transportation Systems,
ITS(24), No. 8, August 2023, pp. 8465-8474.
IEEE DOI
2308
Robustness, Computational modeling, Data models,
Mathematical models, Analytical models, Deep learning,
model compression and neural architecture search
BibRef
Wang, R.Q.[Run-Qi],
Yang, L.L.[Lin-Lin],
Chen, H.L.[Han-Lin],
Wang, W.[Wei],
Doermann, D.[David],
Zhang, B.C.[Bao-Chang],
Anti-Bandit for Neural Architecture Search,
IJCV(131), No. 10, October 2023, pp. 2682-2698.
Springer DOI
2309
BibRef
Chen, H.L.[Han-Lin],
Zhang, B.C.[Bao-Chang],
Xue, S.[Song],
Gong, X.[Xuan],
Liu, H.[Hong],
Ji, R.R.[Rong-Rong],
Doermann, D.[David],
Anti-Bandit Neural Architecture Search for Model Defense,
ECCV20(XIII:70-85).
Springer DOI
2011
Logic gates, Artificial neural networks, Training, Encoding,
Measurement, Data processing, Neural architecture search,
ranking loss
BibRef
Rao, Y.[Yunbo],
Xu, P.[Ping],
Zeng, S.N.[Shao-Ning],
Gou, J.P.[Jian-Ping],
Point completion by a Stack-Style Folding Network with multi-scaled
graphical features,
IET-CV(17), No. 5, 2023, pp. 576-585.
DOI Link
2309
computer vision, convolutional neural nets, neural net architecture
BibRef
Chen, B.[Bohong],
Lin, M.[Mingbao],
Ji, R.R.[Rong-Rong],
Cao, L.J.[Liu-Juan],
Prioritized Subnet Sampling for Resource-Adaptive Supernet Training,
PAMI(45), No. 9, September 2023, pp. 11108-11119.
IEEE DOI
2309
WWW Link.
BibRef
Lacharme, G.[Guillaume],
Cardot, H.[Hubert],
Lenté, C.[Christophe],
Monmarché, N.[Nicolas],
DARTS with Degeneracy Correction,
IbPRIA23(40-53).
Springer DOI
2307
BibRef
Li, Z.W.[Zhuo-Wei],
Gao, Y.[Yibo],
Zha, Z.Z.[Zhen-Zhou],
Hu, Z.Q.[Zhi-Qiang],
Xia, Q.[Qing],
Zhang, S.T.[Shao-Ting],
Metaxas, D.N.[Dimitris N.],
Towards Self-supervised and Weight-preserving Neural Architecture
Search,
SelfLearn22(3-19).
Springer DOI
2304
BibRef
Li, Y.H.[Yun-Hong],
Li, S.[Shuai],
Yu, Z.H.[Zhen-Hua],
DARTS-PAP: Differentiable Neural Architecture Search by Polarization of
Instance Complexity Weighted Architecture Parameters,
MMMod23(II: 277-288).
Springer DOI
2304
BibRef
Yang, T.[Taojiannan],
Yang, L.J.[Lin-Jie],
Jin, X.J.[Xiao-Jie],
Chen, C.[Chen],
Revisiting Training-free NAS Metrics:
An Efficient Training-based Method,
WACV23(4740-4749)
IEEE DOI
2302
Measurement, Costs, Systematics, Correlation, Error analysis,
Graphics processing units, visual reasoning
BibRef
Cavagnero, N.[Niccolň],
Robbiano, L.[Luca],
Caputo, B.[Barbara],
Averta, G.[Giuseppe],
FreeREA: Training-Free Evolution-based Architecture Search,
WACV23(1493-1502)
IEEE DOI
2302
Training, Measurement, Costs, Computational modeling, Search methods,
Neural networks, Memory management, visual reasoning
BibRef
Vu, T.[Thanh],
Zhou, Y.Q.[Yan-Qi],
Wen, C.F.[Chun-Feng],
Li, Y.[Yueqi],
Frahm, J.M.[Jan-Michael],
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task
Neural Architecture Search,
WACV23(1400-1410)
IEEE DOI
2302
Training, Transfer learning, Benchmark testing, Multitasking,
Boosting, Algorithms: Machine learning architectures,
Embedded sensing/real-time techniques
BibRef
Yu, Z.[Zhewen],
Bouganis, C.S.[Christos-Savvas],
SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture
Search,
WACV23(1503-1512)
IEEE DOI
2302
Degradation, Deep learning, Couplings, Neural networks,
Space exploration, Algorithms: Machine learning architectures,
and algorithms (including transfer)
BibRef
Das, M.[Mayukh],
Singh, B.[Brijraj],
Chheda, H.K.[Harsh K.],
Sharma, P.[Pawan],
NS, P.[Pradeep],
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping
Reinforcement,
ICPR22(2568-2574)
IEEE DOI
2212
Training, Power demand, Production, Search problems, Hardware,
Behavioral sciences
BibRef
Hu, Y.[Yue],
Shen, C.[Chongfei],
Yang, L.X.[Li-Xin],
Wu, Z.P.[Zhi-Peng],
Liu, Y.[Yu],
A Novel Predictor with Optimized Sampling Method for Hardware-aware
NAS,
ICPR22(2114-2120)
IEEE DOI
2212
Training, Semiconductor device measurement, Neural networks,
Network architecture, Sampling methods, Hardware
BibRef
Nguyen, X.S.[Xuan Son],
A Gyrovector Space Approach for Symmetric Positive Semi-definite Matrix
Learning,
ECCV22(XXVII:52-68).
Springer DOI
2211
BibRef
Liu, Z.[Zechun],
Shen, Z.Q.[Zhi-Qiang],
Long, Y.[Yun],
Xing, E.[Eric],
Cheng, K.T.[Kwang-Ting],
Leichner, C.[Chas],
Data-Free Neural Architecture Search via Recursive Label Calibration,
ECCV22(XXIV:391-406).
Springer DOI
2211
BibRef
Wang, Q.[Qiang],
Shi, S.[Shaohuai],
Zhao, K.[Kaiyong],
Chu, X.W.[Xiao-Wen],
EASNet: Searching Elastic and Accurate Network Architecture for Stereo
Matching,
ECCV22(XXXII:437-453).
Springer DOI
2211
BibRef
He, W.[Wei],
Yao, Q.[Quanming],
Yokoya, N.[Naoto],
Uezato, T.[Tatsumi],
Zhang, H.Y.[Hong-Yan],
Zhang, L.P.[Liang-Pei],
Spectrum-Aware and Transferable Architecture Search for Hyperspectral
Image Restoration,
ECCV22(XIX:19-37).
Springer DOI
2211
BibRef
Lukasik, J.[Jovita],
Jung, S.[Steffen],
Keuper, M.[Margret],
Learning Where to Look: Generative NAS is Surprisingly Efficient,
ECCV22(XXIII:257-273).
Springer DOI
2211
BibRef
Prach, B.[Bernd],
Lampert, C.H.[Christoph H.],
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz
Networks,
ECCV22(XXI:350-365).
Springer DOI
2211
WWW Link.
BibRef
Zhao, Y.[Yue],
Chen, J.Z.[Jun-Zhou],
Zhang, Z.[Zirui],
Zhang, R.H.[Rong-Hui],
BA-Net: Bridge Attention for Deep Convolutional Neural Networks,
ECCV22(XXI:297-312).
Springer DOI
2211
WWW Link.
BibRef
Davis, J.[Jim],
Frank, L.[Logan],
Revisiting Batch Norm Initialization,
ECCV22(XXI:212-228).
Springer DOI
2211
WWW Link.
BibRef
Trimmel, M.[Martin],
Zanfir, M.[Mihai],
Hartley, R.I.[Richard I.],
Sminchisescu, C.[Cristian],
ERA: Enhanced Rational Activations,
ECCV22(XX:722-738).
Springer DOI
2211
ReLU
BibRef
Yuan, W.T.[Wen-Tao],
Zhu, Q.T.[Qing-Tian],
Liu, X.Y.[Xiang-Yue],
Ding, Y.K.[Yi-Kang],
Zhang, H.T.[Hao-Tian],
Zhang, C.[Chi],
Sobolev Training for Implicit Neural Representations with Approximated
Image Derivatives,
ECCV22(XV:72-88).
Springer DOI
2211
WWW Link.
BibRef
Fan, Z.W.[Zhi-Wen],
Jiang, Y.F.[Yi-Fan],
Wang, P.H.[Pei-Hao],
Gong, X.Y.[Xin-Yu],
Xu, D.[Dejia],
Wang, Z.Y.[Zhang-Yang],
Unified Implicit Neural Stylization,
ECCV22(XV:636-654).
Springer DOI
2211
BibRef
Chen, Y.[Yinbo],
Wang, X.L.[Xiao-Long],
Transformers as Meta-learners for Implicit Neural Representations,
ECCV22(XVII:170-187).
Springer DOI
2211
BibRef
Saragadam, V.[Vishwanath],
Tan, J.[Jasper],
Balakrishnan, G.[Guha],
Baraniuk, R.G.[Richard G.],
Veeraraghavan, A.[Ashok],
MINER: Multiscale Implicit Neural Representation,
ECCV22(XXIII:318-333).
Springer DOI
2211
BibRef
Strümpler, Y.[Yannick],
Postels, J.[Janis],
Yang, R.[Ren],
Van Gool, L.J.[Luc J.],
Tombari, F.[Federico],
Implicit Neural Representations for Image Compression,
ECCV22(XXVI:74-91).
Springer DOI
2211
BibRef
Zhou, Z.X.[Zi-Xuan],
Ning, X.F.[Xue-Fei],
Cai, Y.[Yi],
Han, J.[Jiashu],
Deng, Y.P.[Yi-Ping],
Dong, Y.[Yuhan],
Yang, H.Z.[Hua-Zhong],
Wang, Y.[Yu],
CLOSE: Curriculum Learning on the Sharing Extent Towards Better
One-Shot NAS,
ECCV22(XX:578-594).
Springer DOI
2211
BibRef
Yun, J.[Juseung],
Lee, J.[Janghyeon],
Shon, H.[Hyounguk],
Yi, E.[Eojindl],
Kim, S.H.[Seung Hwan],
Kim, J.[Junmo],
On the Angular Update and Hyperparameter Tuning of a Scale-Invariant
Network,
ECCV22(XII:121-136).
Springer DOI
2211
BibRef
Dutson, M.[Matthew],
Li, Y.[Yin],
Gupta, M.[Mohit],
Event Neural Networks,
ECCV22(XI:276-293).
Springer DOI
2211
BibRef
Qian, Y.G.[Ya-Guan],
Huang, S.H.[Sheng-Hui],
Wang, B.[Bin],
Ling, X.[Xiang],
Guan, X.H.[Xiao-Hui],
Gu, Z.Q.[Zhao-Quan],
Zeng, S.N.[Shao-Ning],
Zhou, W.[Wujie],
Wang, H.J.[Hai-Jiang],
Robust Network Architecture Search via Feature Distortion Restraining,
ECCV22(V:122-138).
Springer DOI
2211
BibRef
You, H.R.[Hao-Ran],
Li, B.[Baopu],
Sun, Z.Y.[Zhan-Yi],
Ouyang, X.[Xu],
Lin, Y.Y.[Ying-Yan],
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via
Jointly Architecture Searching and Parameter Pruning,
ECCV22(XI:674-690).
Springer DOI
2211
BibRef
Yüzügüler, A.C.[Ahmet Caner],
Dimitriadis, N.[Nikolaos],
Frossard, P.[Pascal],
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture
Search,
ECCV22(XII:173-190).
Springer DOI
2211
BibRef
Cai, H.[He],
Zhang, Z.[Zhaokai],
Feng, T.[Tianpeng],
Guo, Y.D.[Yan-Dong],
DARTS-PD: Differentiable Architecture Search with Path-Wise Weight
Sharing Derivation,
ICIP22(1256-1260)
IEEE DOI
2211
Search methods, Artificial neural networks, Optimization,
Neural Architecture Search, path-wise weight sharing derivation
BibRef
Pourchot, A.[Aloďs],
Bailly, K.[Kévin],
Ducarouge, A.[Alexis],
Sigaud, O.[Olivier],
Neural Architecture Search for Fracture Classification,
ICIP22(3226-3230)
IEEE DOI
2211
Protocols, Computational modeling, Transfer learning,
Search problems, Computational efficiency, Fracture Classification
BibRef
Ying, G.H.[Guo-Hao],
He, X.[Xin],
Gao, B.[Bin],
Han, B.[Bo],
Chu, X.W.[Xiao-Wen],
EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs,
ECCV22(XVI:37-53).
Springer DOI
2211
BibRef
Wang, X.X.[Xiao-Xing],
Lin, J.[Jiale],
Zhao, J.[Juanping],
Yang, X.K.[Xiao-Kang],
Yan, J.C.[Jun-Chi],
EAutoDet: Efficient Architecture Search for Object Detection,
ECCV22(XX:668-684).
Springer DOI
2211
BibRef
Xue, C.[Chao],
Wang, X.X.[Xiao-Xing],
Yan, J.C.[Jun-Chi],
Li, C.G.[Chun-Guang],
A Max-Flow Based Approach for Neural Architecture Search,
ECCV22(XX:685-701).
Springer DOI
2211
BibRef
Liu, J.[Jihao],
Huang, X.[Xin],
Song, G.[Guanglu],
Li, H.S.[Hong-Sheng],
Liu, Y.[Yu],
UniNet: Unified Architecture Search with Convolution, Transformer, and
MLP,
ECCV22(XXI:33-49).
Springer DOI
2211
BibRef
Su, X.[Xiu],
You, S.[Shan],
Xie, J.[Jiyang],
Zheng, M.[Mingkai],
Wang, F.[Fei],
Qian, C.[Chen],
Zhang, C.S.[Chang-Shui],
Wang, X.G.[Xiao-Gang],
Xu, C.[Chang],
ViTAS: Vision Transformer Architecture Search,
ECCV22(XXI:139-157).
Springer DOI
2211
BibRef
Liu, C.X.[Chen-Xi],
Leng, Z.Q.[Zhao-Qi],
Sun, P.[Pei],
Cheng, S.Y.[Shu-Yang],
Qi, C.R.[Charles R.],
Zhou, Y.[Yin],
Tan, M.X.[Ming-Xing],
Anguelov, D.[Dragomir],
LidarNAS: Unifying and Searching Neural Architectures for 3D Point
Clouds,
ECCV22(XXI:158-175).
Springer DOI
2211
BibRef
Utasi, Á.[Ákos],
PEA: Improving the Performance of ReLU Networks for Free by Using
Progressive Ensemble Activations,
ECV22(2797-2805)
IEEE DOI
2210
Training, Image segmentation, Semantics,
Neural networks, Network architecture
BibRef
Zhang, M.[Miao],
Pan, S.R.[Shi-Rui],
Chang, X.J.[Xiao-Jun],
Su, S.[Steven],
Hu, J.L.[Ji-Lin],
Haffari, G.[Gholamreza],
Yang, B.[Bin],
BaLeNAS:
Differentiable Architecture Search via the Bayesian Learning Rule,
CVPR22(11861-11870)
IEEE DOI
2210
Deep learning, Costs, Memory management, Optimization methods,
Benchmark testing, Gaussian distribution,
Optimization methods
BibRef
Xiao, H.[Han],
Wang, Z.[Ziwei],
Zhu, Z.[Zheng],
Zhou, J.[Jie],
Lu, J.W.[Ji-Wen],
Shapley-NAS: Discovering Operation Contribution for Neural
Architecture Search,
CVPR22(11882-11891)
IEEE DOI
2210
Costs, Monte Carlo methods, Fluctuations, Codes,
Approximation algorithms,
Deep learning architectures and techniques
BibRef
Huang, T.[Tao],
You, S.[Shan],
Wang, F.[Fei],
Qian, C.[Chen],
Zhang, C.S.[Chang-Shui],
Wang, X.G.[Xiao-Gang],
Xu, C.[Chang],
GreedyNASv2: Greedier Search with a Greedy Path Filter,
CVPR22(11892-11901)
IEEE DOI
2210
Training, Pattern recognition, Reliability,
Deep learning architectures and techniques, retrieval
BibRef
Ding, X.H.[Xiao-Han],
Zhang, X.Y.[Xiang-Yu],
Han, J.G.[Jun-Gong],
Ding, G.G.[Gui-Guang],
Scaling Up Your Kernels to 31X31:
Revisiting Large Kernel Design in CNNs,
CVPR22(11953-11965)
IEEE DOI
2210
Convolutional codes, Shape, Scalability, Transformers, Data models,
Pattern recognition, Convolutional neural networks,
Deep learning architectures and techniques
BibRef
Lin, F.Q.[Fan-Qing],
Price, B.[Brian],
Martinez, T.[Tony],
Generalizing Interactive Backpropagating Refinement for Dense
Prediction Networks,
CVPR22(763-772)
IEEE DOI
2210
Deep learning, Image segmentation, Visualization, Shape, Semantics,
Estimation, Deep learning architectures and techniques,
Vision applications and systems
BibRef
Zhou, Q.Q.[Qin-Qin],
Sheng, K.[Kekai],
Zheng, X.[Xiawu],
Li, K.[Ke],
Sun, X.[Xing],
Tian, Y.H.[Yong-Hong],
Chen, J.[Jie],
Ji, R.R.[Rong-Rong],
Training-free Transformer Architecture Search,
CVPR22(10884-10893)
IEEE DOI
2210
Graphics processing units, Transformers,
Pattern recognition, Task analysis, Explainable computer vision
BibRef
Ye, P.[Peng],
Li, B.[Baopu],
Li, Y.[Yikang],
Chen, T.[Tao],
Fan, J.Y.[Jia-Yuan],
Ouyang, W.L.[Wan-Li],
beta-DARTS: Beta-Decay Regularization for Differentiable Architecture
Search,
CVPR22(10864-10873)
IEEE DOI
2210
Training, Deep learning, Costs, Neural networks,
Search problems, retrieval
BibRef
Hendrickx, L.[Lotte],
van Ranst, W.[Wiebe],
Goedemé, T.[Toon],
Hot-started NAS for Task-specific Embedded Applications,
NAS22(1970-1977)
IEEE DOI
2210
Knowledge engineering, Neural networks,
Size measurement, Search problems, Pattern recognition
BibRef
Moser, B.[Brian],
Raue, F.[Federico],
Hees, J.[Jörn],
Dengel, A.[Andreas],
Less is More: Proxy Datasets in NAS approaches,
NAS22(1952-1960)
IEEE DOI
2210
Training, Neural networks, Training data,
Search problems
BibRef
Liu, C.J.[Chuan-Jian],
Han, K.[Kai],
Xiao, A.[An],
Nie, Y.[Ying],
Zhang, W.[Wei],
Wang, Y.H.[Yun-He],
Network Amplification with Efficient MACs Allocation,
NAS22(1932-1941)
IEEE DOI
2210
Statistical analysis, Computational modeling,
Heuristic algorithms, Neural networks, Network architecture,
Dynamic programming
BibRef
Li, W.S.[Wen-Shuo],
Chen, X.H.[Xing-Hao],
Bai, J.Y.[Jin-Yu],
Ning, X.F.[Xue-Fei],
Wang, Y.H.[Yun-He],
Searching for Energy-Efficient Hybrid Adder-Convolution Neural
Networks,
NAS22(1942-1951)
IEEE DOI
2210
Training, Energy consumption, Convolution, Computational modeling,
Neural networks, Computer architecture
BibRef
Geada, R.[Rob],
McGough, A.S.[Andrew Stephen],
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via
Train-Free Metrics,
NAS22(1961-1969)
IEEE DOI
2210
Measurement, Runtime, Heuristic algorithms, Microprocessors,
Neural networks, Manuals
BibRef
Ding, Y.D.[Ya-Dong],
Wu, Y.[Yu],
Huang, C.Y.[Cheng-Yue],
Tang, S.L.[Si-Liang],
Yang, Y.[Yi],
Wei, L.[Longhui],
Zhuang, Y.T.[Yue-Ting],
Tian, Q.[Qi],
Learning to Learn by Jointly Optimizing Neural Architecture and
Weights,
CVPR22(129-138)
IEEE DOI
2210
Training, Backpropagation, Adaptation models,
Computational efficiency,
Self- semi- meta- unsupervised learning
BibRef
Arican, M.E.[Metin Ersin],
Kara, O.[Ozgur],
Bredell, G.[Gustav],
Konukoglu, E.[Ender],
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image
Prior,
CVPR22(1950-1958)
IEEE DOI
2210
Training, Computational modeling, Superresolution,
Image restoration, Pattern recognition,
Self- semi- meta- unsupervised learning
BibRef
Wang, H.X.[Hao-Xiang],
Wang, Y.[Yite],
Sun, R.[Ruoyu],
Li, B.[Bo],
Global Convergence of MAML and Theory-Inspired Neural Architecture
Search for Few-Shot Learning,
CVPR22(9787-9798)
IEEE DOI
2210
Deep learning, Costs, Neural networks, Supervised learning,
Pattern recognition, Kernel,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Pan, J.[Junyi],
Sun, C.[Chong],
Zhou, Y.Z.[Yi-Zhou],
Zhang, Y.[Ying],
Li, C.[Chen],
Distribution Consistent Neural Architecture Search,
CVPR22(10874-10883)
IEEE DOI
2210
Training, Couplings, Weight measurement, Computational modeling,
Benchmark testing, Search problems, retrieval
BibRef
Mok, J.[Jisoo],
Na, B.[Byunggook],
Kim, J.H.[Ji-Hoon],
Han, D.Y.[Dong-Yoon],
Yoon, S.[Sungroh],
Demystifying the Neural Tangent Kernel from a Practical Perspective:
Can it be trusted for Neural Architecture Search without training?,
CVPR22(11851-11860)
IEEE DOI
2210
Training, Measurement, Costs, Correlation,
Pattern recognition, Deep learning architectures and techniques
BibRef
Huang, M.B.[Min-Bin],
Huang, Z.J.[Zhi-Jian],
Li, C.L.[Chang-Lin],
Chen, X.[Xin],
Xu, H.[Hang],
Li, Z.G.[Zhen-Guo],
Liang, X.D.[Xiao-Dan],
Arch-Graph: Acyclic Architecture Relation Predictor for
Task-Transferable Neural Architecture Search,
CVPR22(11871-11881)
IEEE DOI
2210
Knowledge engineering, Correlation,
Predictive models, Prediction algorithms, Multitasking,
Transfer/low-shot/long-tail learning
BibRef
Zheng, X.[Xiawu],
Fei, X.[Xiang],
Zhang, L.[Lei],
Wu, C.[Chenglin],
Chao, F.[Fei],
Liu, J.Z.[Jian-Zhuang],
Zeng, W.[Wei],
Tian, Y.H.[Yong-Hong],
Ji, R.R.[Rong-Rong],
Neural Architecture Search with Representation Mutual Information,
CVPR22(11902-11911)
IEEE DOI
2210
Training, Performance evaluation, Deep learning, Architecture,
Estimation, Pattern recognition,
Efficient learning and inferences
BibRef
Liu, S.[Shuang],
Okatani, T.[Takayuki],
Symmetry-aware Neural Architecture for Embodied Visual Exploration,
CVPR22(17221-17230)
IEEE DOI
2210
Training, Visualization, Simultaneous localization and mapping,
Convolution, Neural networks, Training data,
Robot vision
BibRef
Peng, C.[Cheng],
Myronenko, A.[Andriy],
Hatamizadeh, A.[Ali],
Nath, V.[Vishwesh],
Siddiquee, M.M.R.[Md Mahfuzur Rahman],
He, Y.F.[Yu-Fan],
Xu, D.[Daguang],
Chellappa, R.[Rama],
Yang, D.[Dong],
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D
Medical Image Segmentation using HyperNet,
CVPR22(20709-20719)
IEEE DOI
2210
Training, Image segmentation, Shape,
Network architecture, Topology, Medical, grouping and shape analysis
BibRef
Xu, K.[Kepeng],
He, G.[Gang],
DNAS:A Decoupled Global Neural Architecture Search Method,
NAS22(1978-1984)
IEEE DOI
2210
Analytical models, Search methods,
Benchmark testing, Pattern recognition
BibRef
Akin, B.[Berkin],
Gupta, S.[Suyog],
Long, Y.[Yun],
Spiridonov, A.[Anton],
Wang, Z.[Zhuo],
White, M.[Marie],
Xu, H.[Hao],
Zhou, P.[Ping],
Zhou, Y.Q.[Yan-Qi],
Searching for Efficient Neural Architectures for On-Device ML on Edge
TPUs,
ECV22(2666-2675)
IEEE DOI
2210
Tensors, Costs, Convolution, Image edge detection,
Throughput
BibRef
Qian, G.[Guocheng],
Zhang, X.[Xuanyang],
Li, G.H.[Guo-Hao],
Zhao, C.[Chen],
Chen, Y.[Yukang],
Zhang, X.Y.[Xiang-Yu],
Ghanem, B.[Bernard],
Sun, J.[Jian],
When NAS Meets Trees:
An Efficient Algorithm for Neural Architecture Search,
ECV22(2781-2786)
IEEE DOI
2210
Costs, Codes, Graphics processing units,
Pattern recognition
BibRef
Courtois, A.[Adrien],
Morel, J.M.[Jean-Michel],
Arias, P.[Pablo],
Investigating Neural Architectures by Synthetic Dataset Design,
VDU22(4886-4895)
IEEE DOI
2210
Systematics, Neural networks, Buildings, Estimation, Computer architecture
BibRef
Chen, Z.[Ziye],
Zhan, Y.B.[Yi-Bing],
Yu, B.[Baosheng],
Gong, M.M.[Ming-Ming],
Du, B.[Bo],
Not All Operations Contribute Equally: Hierarchical
Operation-adaptive Predictor for Neural Architecture Search,
ICCV21(10488-10497)
IEEE DOI
2203
Microprocessors, Logic gates,
Representation learning, Recognition and classification
BibRef
Wang, R.C.[Ruo-Chen],
Chen, X.N.[Xiang-Ning],
Cheng, M.[Minhao],
Tang, X.C.[Xiao-Cheng],
Hsieh, C.J.[Cho-Jui],
RANK-NOSH: Efficient Predictor-Based Architecture Search via
Non-Uniform Successive Halving,
ICCV21(10357-10366)
IEEE DOI
2203
Training, Costs, Scheduling algorithms,
Prediction algorithms, Computational efficiency,
Representation learning
BibRef
Ci, Y.Z.[Yuan-Zheng],
Lin, C.[Chen],
Sun, M.[Ming],
Chen, B.[Boyu],
Zhang, H.W.[Hong-Wen],
Ouyang, W.L.[Wan-Li],
Evolving Search Space for Neural Architecture Search,
ICCV21(6639-6649)
IEEE DOI
2203
Codes, Automation, Extraterrestrial phenomena,
Performance gain, Search problems,
BibRef
Chen, Y.[Ying],
Mao, F.[Feng],
Song, J.[Jie],
Wang, X.C.[Xin-Chao],
Wang, H.Q.[Hui-Qiong],
Song, M.L.[Ming-Li],
Self-born Wiring for Neural Trees,
ICCV21(5027-5036)
IEEE DOI
2203
Wiring, Deep learning, Representation learning, Greedy algorithms,
Scalability, Neural networks, Explainable AI
BibRef
Xue, S.[Song],
Wang, R.[Runqi],
Zhang, B.C.[Bao-Chang],
Wang, T.[Tian],
Guo, G.D.[Guo-Dong],
Doermann, D.[David],
IDARTS: Interactive Differentiable Architecture Search,
ICCV21(1143-1152)
IEEE DOI
2203
Training, Couplings, Backpropagation, Backtracking, Costs,
Recognition and classification,
Optimization and learning methods
BibRef
Deng, C.[Congyue],
Litany, O.[Or],
Duan, Y.[Yueqi],
Poulenard, A.[Adrien],
Tagliasacchi, A.[Andrea],
Guibas, L.J.[Leonidas J.],
Vector Neurons: A General Framework for SO(3)-Equivariant Networks,
ICCV21(12180-12189)
IEEE DOI
2203
Geometry, Shape, Neurons,
Network architecture, Task analysis, 3D from multiview and other sensors
BibRef
Chu, X.X.[Xiang-Xiang],
Zhang, B.[Bo],
Xu, R.J.[Rui-Jun],
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural
Architecture Search,
ICCV21(12219-12228)
IEEE DOI
2203
Training, Computational modeling, Pipelines,
Graphics processing units,
Recognition and classification
BibRef
Moons, B.[Bert],
Noorzad, P.[Parham],
Skliar, A.[Andrii],
Mariani, G.[Giovanni],
Mehta, D.[Dushyant],
Lott, C.[Chris],
Blankevoort, T.[Tijmen],
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces,
ICCV21(12209-12218)
IEEE DOI
2203
Knowledge engineering, Image coding, Pipelines, Neural networks,
Graphics processing units,
grouping and shape
BibRef
Liu, Y.Q.[Yu-Qiao],
Tang, Y.[Yehui],
Sun, Y.[Yanan],
Homogeneous Architecture Augmentation for Neural Predictor,
ICCV21(12229-12238)
IEEE DOI
2203
Training, Performance evaluation, Deep learning, Neural networks,
Training data, Transforms,
BibRef
Wang, Y.M.[Yao-Ming],
Liu, Y.C.[Yu-Chen],
Dai, W.R.[Wen-Rui],
Li, C.L.[Cheng-Lin],
Zou, J.[Junni],
Xiong, H.K.[Hong-Kai],
Learning Latent Architectural Distribution in Differentiable Neural
Architecture Search via Variational Information Maximization,
ICCV21(12292-12301)
IEEE DOI
2203
Error analysis, Search problems,
Data models, Convolutional neural networks, Mutual information,
BibRef
Mok, J.[Jisoo],
Na, B.G.[Byung-Gook],
Choe, H.[Hyeokjun],
Yoon, S.[Sungroh],
AdvRush: Searching for Adversarially Robust Neural Architectures,
ICCV21(12302-12312)
IEEE DOI
2203
Training, Deep learning, Neural networks,
Benchmark testing, Linear programming,
BibRef
Peng, J.F.[Jie-Feng],
Zhang, J.Q.[Ji-Qi],
Li, C.L.[Chang-Lin],
Wang, G.R.[Guang-Run],
Liang, X.D.[Xiao-Dan],
Lin, L.[Liang],
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet
Training Consistency Shift,
ICCV21(12334-12344)
IEEE DOI
2203
Training, Correlation, Search problems,
Task analysis, Machine learning architectures and formulations,
Recognition and classification
BibRef
Chen, B.[Boyu],
Li, P.X.[Pei-Xia],
Li, C.[Chuming],
Li, B.[Baopu],
Bai, L.[Lei],
Lin, C.[Chen],
Sun, M.[Ming],
Yan, J.J.[Jun-Jie],
Ouyang, W.L.[Wan-Li],
GLiT: Neural Architecture Search for Global and Local Image
Transformer,
ICCV21(12-21)
IEEE DOI
2203
Visualization, Image recognition, Correlation,
Evolutionary computation, Transformers,
BibRef
Chen, B.[Boyu],
Li, P.X.[Pei-Xia],
Li, B.[Baopu],
Lin, C.[Chen],
Li, C.[Chuming],
Sun, M.[Ming],
Yan, J.J.[Jun-Jie],
Ouyang, W.L.[Wan-Li],
BN-NAS: Neural Architecture Search with Batch Normalization,
ICCV21(307-316)
IEEE DOI
2203
Training, Codes, Network architecture,
Convergence, Recognition and classification,
BibRef
Yuan, K.[Kun],
Li, Q.Q.[Quan-Quan],
Guo, S.P.[Shao-Peng],
Chen, D.P.[Da-Peng],
Zhou, A.[Aojun],
Yu, F.W.[Feng-Wei],
Liu, Z.[Ziwei],
Differentiable Dynamic Wirings for Neural Networks,
ICCV21(317-326)
IEEE DOI
2203
Wiring, Training, Costs, Computational modeling, Aggregates,
Neural networks,
Efficient training and inference methods
BibRef
Zhou, D.Q.[Da-Quan],
Jin, X.J.[Xiao-Jie],
Lian, X.C.[Xiao-Chen],
Yang, L.J.[Lin-Jie],
Xue, Y.J.[Yu-Jing],
Hou, Q.B.[Qi-Bin],
Feng, J.S.[Jia-Shi],
AutoSpace: Neural Architecture Search with Less Human Interference,
ICCV21(327-336)
IEEE DOI
2203
Knowledge engineering, Costs, Computational modeling,
Interference, Manuals,
Machine learning architectures and formulations
BibRef
Hu, J.[Jie],
Cao, L.J.[Liu-Juan],
Tong, T.[Tong],
Ye, Q.X.[Qi-Xiang],
Zhang, S.C.[Sheng-Chuan],
Li, K.[Ke],
Huang, F.Y.[Fei-Yue],
Shao, L.[Ling],
Ji, R.R.[Rong-Rong],
Architecture Disentanglement for Deep Neural Networks,
ICCV21(652-661)
IEEE DOI
2203
Deep learning, Codes, Semantics, Neural networks,
Network architecture, Explainable AI,
Representation learning
BibRef
Lin, M.[Ming],
Wang, P.[Pichao],
Sun, Z.H.[Zhen-Hong],
Chen, H.[Hesen],
Sun, X.[Xiuyu],
Qian, Q.[Qi],
Li, H.[Hao],
Jin, R.[Rong],
Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition,
ICCV21(337-346)
IEEE DOI
2203
Training, Image recognition, Architecture, Computational modeling,
Graphics processing units,
Machine learning architectures and formulations
BibRef
Jeevan, P.[Pranav],
Sethi, A.[Amit],
Resource-efficient Hybrid X-formers for Vision,
WACV22(3555-3563)
IEEE DOI
2202
Computational modeling, Memory management,
Graphics processing units, Training data, Transformers, Scene Understanding
BibRef
Simon, C.[Christian],
Koniusz, P.[Piotr],
Petersson, L.[Lars],
Han, Y.[Yan],
Harandi, M.[Mehrtash],
Towards a Robust Differentiable Architecture Search under Label Noise,
WACV22(3584-3594)
IEEE DOI
2202
Convolution, Neural networks, Focusing,
Manuals, Games, Statistical Methods,
Learning and Optimization Deep Learning
BibRef
Yu, T.[Tan],
Li, X.[Xu],
Cai, Y.F.[Yun-Feng],
Sun, M.M.[Ming-Ming],
Li, P.[Ping],
S2-MLP: Spatial-Shift MLP Architecture for Vision,
WACV22(3615-3624)
IEEE DOI
2202
Training, Visualization, Image recognition,
Convolution, Transformers,
Deep Learning vision architecture
BibRef
Gong, X.Y.[Xin-Yu],
Chen, W.Y.[Wu-Yang],
Chen, T.L.[Tian-Long],
Wang, Z.Y.[Zhang-Yang],
Sandwich Batch Normalization:
A Drop-In Replacement for Feature Distribution Heterogeneity,
WACV22(2957-2967)
IEEE DOI
2202
Code, Normalization.
WWW Link. Build on DARTS.
Training, Codes, Image synthesis,
Semisupervised learning, Data models, Robustness, Deep Learning
BibRef
Wan, X.C.[Xing-Chen],
Ru, B.X.[Bin-Xin],
Esparança, P.M.[Pedro M.],
Carlucci, F.M.[Fabio M.],
Approximate Neural Architecture Search via Operation Distribution
Learning,
WACV22(3545-3554)
IEEE DOI
2202
Costs, Microprocessors,
Search problems, Encoding, Robustness,
Deep Learning neural network architectures
BibRef
Xia, X.[Xin],
Xiao, X.F.[Xue-Feng],
Wang, X.[Xing],
Zheng, M.[Min],
Progressive Automatic Design of Search Space for One-Shot Neural
Architecture Search,
WACV22(3525-3534)
IEEE DOI
2202
Couplings, Costs, Neural networks,
Search problems, Hardware,
Deep Learning Deep Learning -> Efficient Training and
Inference Methods for Networks
BibRef
Henmi, T.[Takahiko],
Zara, E.R.R.[Esmeraldo Ronnie Rey],
Hirohashi, Y.[Yoshihiro],
Kato, T.[Tsuyoshi],
Adaptive Signal Variances:
CNN Initialization Through Modern Architectures,
ICIP21(374-378)
IEEE DOI
2201
Adaptation models, Convolution, Image processing,
Stability analysis, Convolutional neural networks, Standards, Initialization
BibRef
Gupta, S.[Shashank],
Robles-Kelly, A.[Antonio],
Feature-Extracting Functions for Neural Logic Rule Learning,
DICTA20(1-2)
IEEE DOI
2201
Domain knowledge into behavior of neural network.
Training, Knowledge engineering, Sentiment analysis,
Neural networks, Programming, Feature extraction, rule learning
BibRef
Imamura, A.[Akihiro],
Arizumi, N.[Nana],
Gabor Filter Incorporated CNN for Compression,
IVCNZ21(1-5)
IEEE DOI
2201
Gabor in early layers of CNN.
Image coding, Dictionaries, Costs, Convolution, Real-time systems,
Gabor filters, compression, CNN, Gabor
BibRef
Chitty-Venkata, K.T.[Krishna Teja],
Somani, A.K.[Arun K.],
Kothandaraman, S.[Sreenivas],
Searching Architecture and Precision for U-net based Image
Restoration Tasks,
ICIP21(1989-1993)
IEEE DOI
2201
Deep learning, Measurement, Quantization (signal), Tensors,
Computational modeling, Microprocessors,
Mixed Precision
BibRef
Gaudet, C.J.[Chase J.],
Maida, A.S.[Anthony S.],
Removing Dimensional Restrictions on Complex/Hyper-Complex Neural
Networks,
ICIP21(319-323)
IEEE DOI
2201
Image color analysis, Algebra, Quaternions, Neural networks, MIMICs,
Task analysis, cnn, quaternion, complex, multidimensional
BibRef
Chen, Z.D.[Zhen-Duo],
Liu, F.[Feng],
Zhao, Z.L.[Zheng-Lai],
Let Them Choose What They Want: A Multi-Task CNN Architecture
Leveraging Mid-Level Deep Representations for Face Attribute
Classification,
ICIP21(879-883)
IEEE DOI
2201
Correlation, Image processing,
Task analysis, Faces, deep learning, multi-task learning, attention
BibRef
Lin, J.L.[Jun-Liang],
Sung, Y.L.[Yi-Lin],
Hong, C.Y.[Cheng-Yao],
Lee, H.H.[Han-Hung],
Liu, T.L.[Tyng-Luh],
The Maximum a Posterior Estimation of Darts,
ICIP21(419-423)
IEEE DOI
2201
Couplings, Image processing, Estimation, Network architecture,
Benchmark testing, Search problems, Neural Architecture Search,
Deep Learning
BibRef
Jiang, B.[Borui],
Mu, Y.D.[Ya-Dong],
Russian Doll Network: Learning Nested Networks for Sample-Adaptive
Dynamic Inference,
NeruArch21(336-344)
IEEE DOI
2112
Bridges, Computational modeling,
Transforms, Optimization
BibRef
Shen, B.[Biluo],
Xiao, A.[Anqi],
Tian, J.[Jie],
Hu, Z.H.[Zhen-Hua],
PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural
Network,
NeruArch21(365-372)
IEEE DOI
2112
Training, Image segmentation, Semantics,
Transfer learning, Object detection, Computer architecture
BibRef
Liu, C.H.[Chia-Hsiang],
Han, Y.S.[Yu-Shin],
Sung, Y.Y.[Yuan-Yao],
Lee, Y.[Yi],
Chiang, H.Y.[Hung-Yueh],
Wu, K.C.A.[Kai-Chi-Ang],
FOX-NAS: Fast, On-device and Explainable Neural Architecture Search,
LPCV21(789-797)
IEEE DOI
2112
Costs, Search methods, Neural networks,
Graphics processing units, Computer architecture
BibRef
Chatzianastasis, M.[Michail],
Dasoulas, G.[George],
Siolas, G.[Georgios],
Vazirgiannis, M.[Michalis],
Graph-based Neural Architecture Search with Operation Embeddings,
NeruArch21(393-402)
IEEE DOI
2112
Training, Correlation, Pipelines,
Network architecture
BibRef
Hou, P.F.[Peng-Fei],
Jin, Y.[Ying],
Chen, Y.[Yukang],
Single-DARTS: Towards Stable Architecture Search,
NeruArch21(373-382)
IEEE DOI
2112
Systematics, Costs, Codes,
Stability analysis
BibRef
Devaguptapu, C.[Chaitanya],
Agarwal, D.[Devansh],
Mittal, G.[Gaurav],
Gopalani, P.[Pulkit],
Balasubramanian, V.N.[Vineeth N],
On Adversarial Robustness: A Neural Architecture Search perspective,
AROW21(152-161)
IEEE DOI
2112
Training, Measurement, Deep learning, Analytical models,
Network topology, Neural networks, Computer architecture
BibRef
Chu, X.X.[Xiang-Xiang],
Zhang, B.[Bo],
Li, Q.Y.[Qing-Yuan],
Xu, R.J.[Rui-Jun],
Li, X.D.[Xu-Dong],
SCARLET-NAS: Bridging the Gap between Stability and Scalability in
Weight-sharing Neural Architecture Search,
NeruArch21(317-325)
IEEE DOI
2112
Training, Scalability, Perturbation methods,
Stability analysis
BibRef
Zhang, K.[Kaihua],
Dong, M.L.[Ming-Liang],
Liu, B.[Bo],
Yuan, X.T.[Xiao-Tong],
Liu, Q.S.[Qing-Shan],
DeepACG: Co-Saliency Detection via Semantic-aware Contrast
Gromov-Wasserstein Distance,
CVPR21(13698-13707)
IEEE DOI
2111
Image segmentation, Correlation,
Image edge detection, Semantics, Network architecture, Benchmark testing
BibRef
Su, X.[Xiu],
Huang, T.[Tao],
Li, Y.X.[Yan-Xi],
You, S.[Shan],
Wang, F.[Fei],
Qian, C.[Chen],
Zhang, C.S.[Chang-Shui],
Xu, C.[Chang],
Prioritized Architecture Sampling with Monto-Carlo Tree Search,
CVPR21(10963-10972)
IEEE DOI
2111
Training, Monte Carlo methods, Costs, Codes,
Computational modeling, Computer architecture
BibRef
Gao, S.H.[Shang-Hua],
Han, Q.[Qi],
Li, D.[Duo],
Cheng, M.M.[Ming-Ming],
Peng, P.[Pai],
Representative Batch Normalization with Feature Calibration,
CVPR21(8665-8675)
IEEE DOI
2111
Training, Costs, Neural networks, Standardization,
Calibration, Pattern recognition
BibRef
Li, S.[Sheng],
Tan, M.X.[Ming-Xing],
Pang, R.[Ruoming],
Li, A.[Andrew],
Cheng, L.Q.[Li-Qun],
Le, Q.V.[Quoc V.],
Jouppi, N.P.[Norman P.],
Searching for Fast Model Families on Datacenter Accelerators,
CVPR21(8081-8091)
IEEE DOI
2111
Convolutional codes, Convolution,
Computational modeling, Search methods, Parallel processing, Hardware
BibRef
Xu, L.[Lumin],
Guan, Y.[Yingda],
Jin, S.[Sheng],
Liu, W.T.[Wen-Tao],
Qian, C.[Chen],
Luo, P.[Ping],
Ouyang, W.L.[Wan-Li],
Wang, X.G.[Xiao-Gang],
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search,
CVPR21(16067-16076)
IEEE DOI
2111
Training, Costs, Pose estimation,
Streaming media, Real-time systems
BibRef
Cui, Y.F.[Yu-Fei],
Liu, Z.Q.[Zi-Quan],
Li, Q.[Qiao],
Chan, A.B.[Antoni B.],
Xue, C.J.[Chun Jason],
Bayesian Nested Neural Networks for Uncertainty Calibration and
Adaptive Compression,
CVPR21(2392-2401)
IEEE DOI
2111
Training, Uncertainty, Computational modeling,
Neural networks, Data models, Bayes methods
BibRef
Hosseini, R.[Ramtin],
Yang, X.Y.[Xing-Yi],
Xie, P.[Pengtao],
DSRNA: Differentiable Search of Robust Neural Architectures,
CVPR21(6192-6201)
IEEE DOI
2111
Measurement, Jacobian matrices, Deep learning, Training,
Perturbation methods, Search problems
BibRef
Huang, S.Y.[Sian-Yao],
Chu, W.T.[Wei-Ta],
Searching by Generating: Flexible and Efficient One-Shot NAS with
Architecture Generator,
CVPR21(983-992)
IEEE DOI
2111
Training, Costs, Codes, Memory management,
Graphics processing units, Search problems
BibRef
Dai, X.L.[Xiao-Liang],
Wan, A.[Alvin],
Zhang, P.Z.[Pei-Zhao],
Wu, B.C.[Bi-Chen],
He, Z.J.[Zi-Jian],
Wei, Z.[Zhen],
Chen, K.[Kan],
Tian, Y.D.[Yuan-Dong],
Yu, M.[Matthew],
Vajda, P.[Peter],
Gonzalez, J.E.[Joseph E.],
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining,
CVPR21(16271-16280)
IEEE DOI
2111
Training, Search methods, Neural networks,
Manuals, Performance gain, Prediction algorithms
BibRef
Xiong, Y.Y.[Yun-Yang],
Liu, H.X.[Han-Xiao],
Gupta, S.[Suyog],
Akin, B.[Berkin],
Bender, G.[Gabriel],
Wang, Y.Z.[Yong-Zhe],
Kindermans, P.J.[Pieter-Jan],
Tan, M.X.[Ming-Xing],
Singh, V.[Vikas],
Chen, B.[Bo],
MobileDets:
Searching for Object Detection Architectures for Mobile Accelerators,
CVPR21(3824-3833)
IEEE DOI
2111
Convolutional codes, Image edge detection, Neural networks,
Object detection, Network architecture, Search problems
BibRef
He, Y.F.[Yu-Fan],
Yang, D.[Dong],
Roth, H.[Holger],
Zhao, C.[Can],
Xu, D.[Daguang],
DiNTS: Differentiable Neural Network Topology Search for 3D Medical
Image Segmentation,
CVPR21(5837-5846)
IEEE DOI
2111
Image segmentation, Solid modeling,
Network topology, Graphics processing units, Benchmark testing, Topology
BibRef
Ding, M.Y.[Ming-Yu],
Lian, X.C.[Xiao-Chen],
Yang, L.J.[Lin-Jie],
Wang, P.[Peng],
Jin, X.J.[Xiao-Jie],
Lu, Z.W.[Zhi-Wu],
Luo, P.[Ping],
HR-NAS: Searching Efficient High-Resolution Neural Architectures with
Lightweight Transformers,
CVPR21(2981-2991)
IEEE DOI
2111
Convolutional codes, Image segmentation, Computational modeling,
Transformers, Search problems, Encoding
BibRef
Li, Y.[Yawei],
Li, W.[Wen],
Danelljan, M.[Martin],
Zhang, K.[Kai],
Gu, S.H.[Shu-Hang],
Van Gool, L.J.[Luc J.],
Timofte, R.[Radu],
The Heterogeneity Hypothesis:
Finding Layer-Wise Differentiated Network Architectures,
CVPR21(2144-2153)
IEEE DOI
2111
Training, Visualization, Protocols, Computational modeling,
Network architecture, Pattern recognition
BibRef
Chen, M.H.[Ming-Hao],
Fu, J.L.[Jian-Long],
Ling, H.B.[Hai-Bin],
One-Shot Neural Ensemble Architecture Search by Diversity-Guided
Search Space Shrinking,
CVPR21(16525-16534)
IEEE DOI
2111
Codes, Benchmark testing,
Extraterrestrial measurements, Robustness, Complexity theory
BibRef
Yan, B.[Bin],
Peng, H.[Houwen],
Wu, K.[Kan],
Wang, D.[Dong],
Fu, J.L.[Jian-Long],
Lu, H.C.[Hu-Chuan],
LightTrack: Finding Lightweight Neural Networks for Object Tracking
via One-Shot Architecture Search,
CVPR21(15175-15184)
IEEE DOI
2111
Oceans, Neural networks, Graphics processing units,
Real-time systems
BibRef
Yan, Z.C.[Zhi-Cheng],
Dai, X.L.[Xiao-Liang],
Zhang, P.Z.[Pei-Zhao],
Tian, Y.D.[Yuan-Dong],
Wu, B.C.[Bi-Chen],
Feiszli, M.[Matt],
FP-NAS: Fast Probabilistic Neural Architecture Search,
CVPR21(15134-15143)
IEEE DOI
2111
Adaptation models, Computational modeling,
Memory management, Probabilistic logic, Sampling methods
BibRef
Li, Z.G.[Zhen-Gang],
Yuan, G.[Geng],
Niu, W.[Wei],
Zhao, P.[Pu],
Li, Y.Y.[Yan-Yu],
Cai, Y.X.[Yu-Xuan],
Shen, X.[Xuan],
Zhan, Z.[Zheng],
Kong, Z.L.[Zheng-Lun],
Jin, Q.[Qing],
Chen, Z.Y.[Zhi-Yu],
Liu, S.J.[Si-Jia],
Yang, K.Y.[Kai-Yuan],
Ren, B.[Bin],
Wang, Y.Z.[Yan-Zhi],
Lin, X.[Xue],
NPAS: A Compiler-aware Framework of Unified Network Pruning and
Architecture Search for Beyond Real-Time Mobile Acceleration,
CVPR21(14250-14261)
IEEE DOI
2111
Training, Performance evaluation, Codes, Computational modeling,
Reinforcement learning, Network architecture
BibRef
Zhang, X.[Xiong],
Xu, H.M.[Hong-Min],
Mo, H.[Hong],
Tan, J.C.[Jian-Chao],
Yang, C.[Cheng],
Wang, L.[Lei],
Ren, W.Q.[Wen-Qi],
DCNAS: Densely Connected Neural Architecture Search for Semantic
Image Segmentation,
CVPR21(13951-13962)
IEEE DOI
2111
Training, Image segmentation, Visualization,
Semantics, Memory management, Network architecture
BibRef
Gu, Y.C.[Yu-Chao],
Wang, L.J.[Li-Juan],
Liu, Y.[Yun],
Yang, Y.[Yi],
Wu, Y.H.[Yu-Huan],
Lu, S.P.[Shao-Ping],
Cheng, M.M.[Ming-Ming],
DOTS: Decoupling Operation and Topology in Differentiable
Architecture Search,
CVPR21(12306-12315)
IEEE DOI
2111
Codes, Microprocessors, Image edge detection, Search problems
BibRef
Liu, H.X.[Han-Xiao],
Simonyan, K.[Karen],
Yang, Y.M.[Yi-Ming],
DARTS: Differentiable architecture search,
ICLR19
WWW Link.
BibRef
1900
Zhang, X.Y.[Xuan-Yang],
Hou, P.F.[Peng-Fei],
Zhang, X.Y.[Xiang-Yu],
Sun, J.[Jian],
Neural Architecture Search with Random Labels,
CVPR21(10902-10911)
IEEE DOI
2111
Training,
Pattern recognition, Task analysis
BibRef
Yang, Z.H.[Zhao-Hui],
Wang, Y.H.[Yun-He],
Chen, X.H.[Xing-Hao],
Guo, J.Y.[Jian-Yuan],
Zhang, W.[Wei],
Xu, C.[Chao],
Xu, C.J.[Chun-Jing],
Tao, D.C.[Da-Cheng],
Xu, C.[Chang],
HourNAS: Extremely Fast Neural Architecture Search Through an
Hourglass Lens,
CVPR21(10891-10901)
IEEE DOI
2111
Deep learning, Costs, Neural networks, Graphics processing units,
Complexity theory, Pattern recognition
BibRef
Liang, T.T.[Ting-Ting],
Wang, Y.T.[Yong-Tao],
Tang, Z.[Zhi],
Hu, G.S.[Guo-Sheng],
Ling, H.B.[Hai-Bin],
OPANAS: One-Shot Path Aggregation Network Architecture Search for
Object Detection,
CVPR21(10190-10198)
IEEE DOI
2111
Training, Visualization, Costs, Graphics processing units,
Object detection, Network architecture
BibRef
Chen, Y.F.[Yao-Fo],
Guo, Y.[Yong],
Chen, Q.[Qi],
Li, M.L.[Min-Li],
Zeng, W.[Wei],
Wang, Y.W.[Yao-Wei],
Tan, M.K.[Ming-Kui],
Contrastive Neural Architecture Search with Neural Architecture
Comparators,
CVPR21(9497-9506)
IEEE DOI
2111
Training data,
Pattern recognition, Computational efficiency, Task analysis
BibRef
Yang, Y.[Yibo],
You, S.[Shan],
Li, H.Y.[Hong-Yang],
Wang, F.[Fei],
Qian, C.[Chen],
Lin, Z.C.[Zhou-Chen],
Towards Improving the Consistency, Efficiency, and Flexibility of
Differentiable Neural Architecture Search,
CVPR21(6663-6672)
IEEE DOI
2111
Training, Costs, Error analysis, Search methods,
Memory management, Computer architecture
BibRef
Cai, S.F.[Shao-Fei],
Li, L.[Liang],
Deng, J.C.[Jin-Can],
Zhang, B.C.[Bei-Chen],
Zha, Z.J.[Zheng-Jun],
Su, L.[Li],
Huang, Q.M.[Qing-Ming],
Rethinking Graph Neural Architecture Search from Message-passing,
CVPR21(6653-6662)
IEEE DOI
2111
Filtering, Message passing, Manuals,
Search problems, Feature extraction, Graph neural networks
BibRef
Wang, D.[Dilin],
Li, M.[Meng],
Gong, C.Y.[Cheng-Yue],
Chandra, V.[Vikas],
AttentiveNAS: Improving Neural Architecture Search via Attentive
Sampling,
CVPR21(6414-6423)
IEEE DOI
2111
Training, Codes, Pattern recognition
BibRef
Duan, Y.W.[Ya-Wen],
Chen, X.[Xin],
Xu, H.[Hang],
Chen, Z.W.[Ze-Wei],
Liang, X.D.[Xiao-Dan],
Zhang, T.[Tong],
Li, Z.G.[Zhen-Guo],
TransNAS-Bench-101: Improving transferability and Generalizability of
Cross-Task Neural Architecture Search,
CVPR21(5247-5256)
IEEE DOI
2111
Training, Knowledge engineering,
Design methodology, Transfer learning, Benchmark testing
BibRef
Xu, Y.X.[Yi-Xing],
Wang, Y.H.[Yun-He],
Han, K.[Kai],
Tang, Y.H.[Ye-Hui],
Jui, S.L.[Shang-Ling],
Xu, C.J.[Chun-Jing],
Xu, C.[Chang],
ReNAS: Relativistic Evaluation of Neural Architecture Search,
CVPR21(4409-4418)
IEEE DOI
2111
Training, Performance evaluation, Tensors, Costs, Microprocessors,
Refining, Estimation
BibRef
Yang, T.J.[Tien-Ju],
Liao, Y.L.[Yi-Lun],
Sze, V.[Vivienne],
NetAdaptV2: Efficient Neural Architecture Search with Fast
Super-Network Training and Architecture Optimization,
CVPR21(2402-2411)
IEEE DOI
2111
Training, Measurement, Deep learning, Technological innovation,
Costs, Estimation, Computer architecture
BibRef
Chu, G.[Grace],
Arikan, O.[Okan],
Bender, G.[Gabriel],
Wang, W.J.[Wei-Jun],
Brighton, A.[Achille],
Kindermans, P.J.[Pieter-Jan],
Liu, H.X.[Han-Xiao],
Akin, B.[Berkin],
Gupta, S.[Suyog],
Howard, A.[Andrew],
Discovering Multi-Hardware Mobile Models via Architecture Search,
ECV21(3016-3025)
IEEE DOI
2109
Graphics processing units, Focusing,
Debugging, Extraterrestrial measurements
BibRef
Peer, D.[David],
Stabinger, S.[Sebastian],
Rodríguez-Sánchez, A.[Antonio],
Conflicting Bundles: Adapting Architectures Towards the Improved
Training of Deep Neural Networks,
WACV21(256-265)
IEEE DOI
2106
Training, Measurement, Adaptation models,
Neural networks, Memory management
BibRef
Alt, T.[Tobias],
Peter, P.[Pascal],
Weickert, J.[Joachim],
Schrader, K.[Karl],
Translating Numerical Concepts for PDEs into Neural Architectures,
SSVM21(294-306).
Springer DOI
2106
BibRef
Tanveer, M.S.[Muhammad Suhaib],
Khan, M.U.K.[Muhammad Umar Karim],
Kyung, C.M.[Chong-Min],
Fine-Tuning DARTS for Image Classification,
ICPR21(4789-4796)
IEEE DOI
2105
Microprocessors, Image classification
BibRef
Tsukahara, T.[Takuya],
Hirakawa, T.[Tsubasa],
Yamashita, T.[Takayoshi],
Fujiyoshi, H.[Hironobu],
Improving reliability of attention branch network by introducing
uncertainty,
ICPR21(1536-1542)
IEEE DOI
2105
Bayesian neural network to account for uncertainty in the answer.
Uncertainty, Image recognition, Bayes methods,
Reliability, Object recognition, Convolutional neural networks
BibRef
Chu, X.X.[Xiang-Xiang],
Zhang, B.[Bo],
Ma, H.L.[Hai-Long],
Xu, R.J.[Rui-Jun],
Li, Q.Y.[Qing-Yuan],
Fast, Accurate and Lightweight Super-Resolution with Neural
Architecture Search,
ICPR21(59-64)
IEEE DOI
2105
Training, Performance evaluation, PSNR, Image coding,
Superresolution, Reinforcement learning
BibRef
Zhang, H.[Huigang],
Wang, L.[Liuan],
Sun, J.[Jun],
Sun, L.[Li],
Kobashi, H.[Hiromichi],
Imamura, N.[Nobutaka],
NAS-EOD: an end-to-end Neural Architecture Search method for
Efficient Object Detection,
ICPR21(1446-1451)
IEEE DOI
2105
Performance evaluation, Training, Adaptation models,
Image edge detection, Search methods, Pipelines, Graphics processing units
BibRef
Jordao, A.[Artur],
Akio, F.[Fernando],
Lie, M.[Maiko],
Schwartz, W.R.[William Robson],
Stage-Wise Neural Architecture Search,
ICPR21(1985-1992)
IEEE DOI
2105
Design methodology, Memory management,
Search problems, Pattern recognition
BibRef
Ruiz, A.[Adria],
Agudo, A.[Antonio],
Moreno-Noguer, F.[Francesc],
Generating Attribution Maps with Disentangled Masked Backpropagation,
ICCV21(885-894)
IEEE DOI
2203
Backpropagation, Visualization, Computational modeling,
Piecewise linear approximation, Neural networks,
BibRef
López, J.G.[Javier García],
Agudo, A.[Antonio],
Moreno-Noguer, F.[Francesc],
E-DNAS: Differentiable Neural Architecture Search for Embedded
Systems,
ICPR21(4704-4711)
IEEE DOI
2105
Measurement, Training, Embedded systems, Search methods,
Presence network agents, System-on-chip, Kernel, Deep Learning,
Convolutional Meta Kernels
BibRef
Ahn, J.Y.[Joon Young],
Cho, N.I.[Nam Ik],
Neural Architecture Search for Image Super-Resolution Using Densely
Constructed Search Space: DeCoNAS,
ICPR21(4829-4836)
IEEE DOI
2105
Superresolution, Network architecture, Search problems,
Complexity theory, Pattern recognition,
Task analysis
BibRef
Peter, D.[David],
Roth, W.[Wolfgang],
Pernkopf, F.[Franz],
Resource-Efficient DNNs for Keyword Spotting using Neural
Architecture Search and Quantization,
ICPR21(9273-9279)
IEEE DOI
2105
Performance evaluation, Quantization (signal), Microcontrollers,
Memory management, Internet, Pattern recognition,
weight quantization
BibRef
Siddiqui, S.[Shahid],
Kyrkou, C.[Christos],
Theocharides, T.[Theocharis],
Operation and Topology Aware Fast Differentiable Architecture Search,
ICPR21(9666-9673)
IEEE DOI
2105
Convolution, Microprocessors, Architecture,
Network architecture, Search problems, Topology
BibRef
Donegan, C.[Ciarán],
Yous, H.[Hamza],
Sinha, S.[Saksham],
Byrne, J.[Jonathan],
VPU Specific CNNs through Neural Architecture Search,
ICPR21(9772-9779)
IEEE DOI
2105
Performance evaluation, Training, Knowledge engineering,
Neural networks, Graphics processing units, Computer architecture
BibRef
La Grassa, R.[Riccardo],
Gallo, I.[Ignazio],
Landro, N.[Nicola],
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft
Grained for Classification Task,
CAIP21(I:393-402).
Springer DOI
2112
BibRef
Gallo, I.[Ignazio],
Magistrali, G.,
Landro, N.[Nicola],
La Grassa, R.[Riccardo],
Improving the Efficient Neural Architecture Search via Rewarding
Modifications,
IVCNZ20(1-6)
IEEE DOI
2012
Training, Deep learning, Recurrent neural networks,
Reinforcement learning, Task analysis,
Classification
BibRef
Yuan, G.,
Xue, B.,
Zhang, M.,
A Graph-Based Approach to Automatic Convolutional Neural Network
Construction for Image Classification,
IVCNZ20(1-6)
IEEE DOI
2012
Neural networks,
Classification algorithms, Convolutional neural networks,
neural architecture search
BibRef
Li, J.H.[Ji-Hao],
Diao, W.H.[Wen-Hui],
Sun, X.[Xian],
Feng, Y.C.[Ying-Chao],
Zhang, W.K.[Wen-Kai],
Chang, Z.H.[Zhong-Han],
Fu, K.[Kun],
Automated and Lightweight Network Design Via Random Search for Remote
Sensing Image Scene Classification,
ISPRS20(B2:1217-1224).
DOI Link
2012
BibRef
Chen, Y.C.[Yun-Chun],
Gao, C.[Chen],
Robb, E.[Esther],
Huang, J.B.[Jia-Bin],
NAS-DIP: Learning Deep Image Prior with Neural Architecture Search,
ECCV20(XVIII:442-459).
Springer DOI
2012
BibRef
Hu, Y.[Yibo],
Wu, X.[Xiang],
He, R.[Ran],
TF-NAS: Rethinking Three Search Freedoms of Latency-constrained
Differentiable Neural Architecture Search,
ECCV20(XV:123-139).
Springer DOI
2011
BibRef
Xu, H.[Hang],
Wang, S.J.[Shao-Ju],
Cai, X.Y.[Xin-Yue],
Zhang, W.[Wei],
Liang, X.D.[Xiao-Dan],
Li, Z.G.[Zhen-Guo],
Curvelane-NAS:
Unifying Lane-sensitive Architecture Search and Adaptive Point Blending,
ECCV20(XV:689-704).
Springer DOI
2011
BibRef
Chu, X.X.[Xiang-Xiang],
Zhou, T.B.[Tian-Bao],
Zhang, B.[Bo],
Li, J.X.[Ji-Xiang],
Fair Darts: Eliminating Unfair Advantages in Differentiable
Architecture Search,
ECCV20(XV:465-480).
Springer DOI
2011
BibRef
Dai, X.Y.[Xi-Yang],
Chen, D.D.[Dong-Dong],
Liu, M.C.[Meng-Chen],
Chen, Y.P.[Yin-Peng],
Yuan, L.[Lu],
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search,
ECCV20(XXVII:584-600).
Springer DOI
2011
BibRef
Hu, Y.[Yutao],
Jiang, X.L.[Xiao-Long],
Liu, X.H.[Xu-Hui],
Zhang, B.C.[Bao-Chang],
Han, J.G.[Jun-Gong],
Cao, X.B.[Xian-Bin],
Doermann, D.[David],
NAS-Count: Counting-by-density with Neural Architecture Search,
ECCV20(XXII:747-766).
Springer DOI
2011
BibRef
Yuan, Q.W.[Qiong-Wen],
He, J.W.[Jing-Wei],
Yu, L.[Lei],
Zheng, G.[Gang],
AIM-Net: Bring Implicit Euler to Network Design,
ICIP20(1926-1930)
IEEE DOI
2011
Neural networks, Adaptation models, Convergence,
Mathematical model, Image resolution, Signal resolution,
image superresolution
BibRef
Howard-Jenkins, H.[Henry],
Li, Y.[Yiwen],
Prisacariu, V.A.[Victor Adrian],
Gross: Group-size Series Decomposition for Grouped Architecture Search,
ECCV20(XXVI:18-33).
Springer DOI
2011
BibRef
Hu, Y.M.[Yi-Ming],
Liang, Y.D.[Yu-Ding],
Guo, Z.C.[Zi-Chao],
Wan, R.[Ruosi],
Zhang, X.Y.[Xiang-Yu],
Wei, Y.C.[Yi-Chen],
Gu, Q.Y.[Qing-Yi],
Sun, J.[Jian],
Angle-based Search Space Shrinking for Neural Architecture Search,
ECCV20(XIX:119-134).
Springer DOI
2011
BibRef
Chen, X.[Xin],
Duan, Y.W.[Ya-Wen],
Chen, Z.W.[Ze-Wei],
Xu, H.[Hang],
Chen, Z.[Zihao],
Liang, X.D.[Xiao-Dan],
Zhang, T.[Tong],
Li, Z.G.[Zhen-Guo],
Catch: Context-based Meta Reinforcement Learning for Transferrable
Architecture Search,
ECCV20(XIX:185-202).
Springer DOI
2011
BibRef
Yu, J.H.[Jia-Hui],
Jin, P.C.[Peng-Chong],
Liu, H.X.[Han-Xiao],
Bender, G.[Gabriel],
Kindermans, P.J.[Pieter-Jan],
Tan, M.X.[Ming-Xing],
Huang, T.[Thomas],
Song, X.D.[Xiao-Dan],
Pang, R.M.[Ruo-Ming],
Le, Q.[Quoc],
Bignas: Scaling up Neural Architecture Search with Big Single-stage
Models,
ECCV20(VII:702-717).
Springer DOI
2011
BibRef
Tian, Y.[Yuan],
Wang, Q.[Qin],
Huang, Z.W.[Zhi-Wu],
Li, W.[Wen],
Dai, D.X.[Deng-Xin],
Yang, M.H.[Ming-Hao],
Wang, J.[Jun],
Fink, O.[Olga],
Off-policy Reinforcement Learning for Efficient and Effective GAN
Architecture Search,
ECCV20(VII:175-192).
Springer DOI
2011
BibRef
Bulat, A.[Adrian],
Martinez, B.[Brais],
Tzimiropoulos, G.[Georgios],
Bats: Binary Architecture Search,
ECCV20(XXIII:309-325).
Springer DOI
2011
BibRef
Yuan, Z.H.[Zhi-Hang],
Wu, B.Z.[Bing-Zhe],
Sun, G.Y.[Guang-Yu],
Liang, Z.[Zheng],
Zhao, S.W.[Shi-Wan],
Bi, W.C.[Wei-Chen],
S2dnas: Transforming Static Cnn Model for Dynamic Inference via Neural
Architecture Search,
ECCV20(II:175-192).
Springer DOI
2011
BibRef
Liu, C.X.[Chen-Xi],
Dollár, P.[Piotr],
He, K.M.[Kai-Ming],
Girshick, R.[Ross],
Yuille, A.L.[Alan L.],
Xie, S.N.[Sai-Ning],
Are Labels Necessary for Neural Architecture Search?,
ECCV20(IV:798-813).
Springer DOI
2011
BibRef
Wen, W.[Wei],
Liu, H.X.[Han-Xiao],
Chen, Y.R.[Yi-Ran],
Li, H.[Hai],
Bender, G.[Gabriel],
Kindermans, P.J.[Pieter-Jan],
Neural Predictor for Neural Architecture Search,
ECCV20(XXIX: 660-676).
Springer DOI
2010
BibRef
Vahdat, A.,
Mallya, A.,
Liu, M.,
Kautz, J.,
UNAS: Differentiable Architecture Search Meets Reinforcement Learning,
CVPR20(11263-11272)
IEEE DOI
2008
Search problems, DNA, Linear programming,
Task analysis, Estimation, Loss measurement
BibRef
Murdock, C.[Calvin],
Lucey, S.[Simon],
Dataless Model Selection With the Deep Frame Potential,
CVPR20(11254-11262)
IEEE DOI
2008
Dictionaries, Sparse representation, Robustness, Coherence,
Neural networks, Machine learning
BibRef
Berman, M.[Maxim],
Pishchulin, L.[Leonid],
Xu, N.[Ning],
Blaschko, M.B.[Matthew B.],
Medioni, G.[Gérard],
AOWS: Adaptive and Optimal Network Width Search With Latency
Constraints,
CVPR20(11214-11223)
IEEE DOI
2008
Training, Computational modeling,
Task analysis, Neural networks, Hardware, Measurement
BibRef
Radosavovic, I.[Ilija],
Kosaraju, R.P.[Raj Prateek],
Girshick, R.[Ross],
He, K.[Kaiming],
Dollár, P.[Piotr],
Designing Network Design Spaces,
CVPR20(10425-10433)
IEEE DOI
2008
Computational modeling, Manuals, Tools, Sociology, Statistics,
Training, Visualization
BibRef
Zoran, D.[Daniel],
Chrzanowski, M.[Mike],
Huang, P.S.[Po-Sen],
Gowal, S.[Sven],
Mott, A.[Alex],
Kohli, P.[Pushmeet],
Towards Robust Image Classification Using Sequential Attention Models,
CVPR20(9480-9489)
IEEE DOI
2008
Augment NN with attention model.
Robustness, Computational modeling,
Adaptation models, Brain modeling, Biological system modeling, Training
BibRef
Huang, L.[Lei],
Zhao, L.[Lei],
Zhou, Y.[Yi],
Zhu, F.[Fan],
Liu, L.[Li],
Shao, L.[Ling],
An Investigation Into the Stochasticity of Batch Whitening,
CVPR20(6438-6447)
IEEE DOI
2008
Training, Principal component analysis, Covariance matrices,
Standardization, Optimization, Sociology
BibRef
Bender, G.,
Liu, H.,
Chen, B.,
Chu, G.,
Cheng, S.,
Kindermans, P.,
Le, Q.V.,
Can Weight Sharing Outperform Random Architecture Search? An
Investigation With TuNAS,
CVPR20(14311-14320)
IEEE DOI
2008
Search problems, Google,
Inference algorithms, Task analysis, Training
BibRef
Kim, E.,
Kang, W.Y.,
On, K.,
Heo, Y.,
Zhang, B.,
Hypergraph Attention Networks for Multimodal Learning,
CVPR20(14569-14578)
IEEE DOI
2008
Semantics, Visualization, Task analysis, Knowledge discovery,
Message passing, Computational modeling, Biological neural networks
BibRef
Lin, R.,
Liu, W.,
Liu, Z.,
Feng, C.,
Yu, Z.,
Rehg, J.M.,
Xiong, L.,
Song, L.,
Regularizing Neural Networks via Minimizing Hyperspherical Energy,
CVPR20(6916-6925)
IEEE DOI
2008
Neurons, Biological neural networks, Training, Optimization,
Task analysis, Testing, Linear programming
BibRef
Wan, A.,
Dai, X.,
Zhang, P.,
He, Z.,
Tian, Y.,
Xie, S.,
Wu, B.,
Yu, M.,
Xu, T.,
Chen, K.,
Vajda, P.,
Gonzalez, J.E.,
FBNetV2: Differentiable Neural Architecture Search for Spatial and
Channel Dimensions,
CVPR20(12962-12971)
IEEE DOI
2008
DNA, Convolution, Neural networks, Training,
Computational efficiency, Space exploration
BibRef
Mozejko, M.,
Latkowski, T.,
Treszczotko, L.,
Szafraniuk, M.,
Trojanowski, K.,
Superkernel Neural Architecture Search for Image Denoising,
NTIRE20(2002-2011)
IEEE DOI
2008
Training, Task analysis, Image denoising, Kernel, Memory management, Graphics processing units
BibRef
Zhang, L.L.,
Yang, Y.,
Jiang, Y.,
Zhu, W.,
Liu, Y.,
Fast Hardware-Aware Neural Architecture Search,
EDLCV20(2959-2967)
IEEE DOI
2008
Hardware, Graphics processing units,
Hurricanes, Training, Measurement
BibRef
Gao, Y.,
Bai, H.,
Jie, Z.,
Ma, J.,
Jia, K.,
Liu, W.,
MTL-NAS: Task-Agnostic Neural Architecture Search Towards
General-Purpose Multi-Task Learning,
CVPR20(11540-11549)
IEEE DOI
2008
Task analysis, Entropy, Neural networks,
Feature extraction, Semantics, Convolution
BibRef
Zhou, D.,
Zhou, X.,
Zhang, W.,
Loy, C.C.,
Yi, S.,
Zhang, X.,
Ouyang, W.,
EcoNAS: Finding Proxies for Economical Neural Architecture Search,
CVPR20(11393-11401)
IEEE DOI
2008
Training, Reliability,
Graphics processing units, Acceleration, Measurement
BibRef
Zheng, X.,
Ji, R.,
Wang, Q.,
Ye, Q.,
Li, Z.,
Tian, Y.,
Tian, Q.,
Rethinking Performance Estimation in Neural Architecture Search,
CVPR20(11353-11362)
IEEE DOI
2008
Estimation, Microprocessors, Training,
Optimization, Search problems, Learning (artificial intelligence)
BibRef
Fang, J.,
Sun, Y.,
Zhang, Q.,
Li, Y.,
Liu, W.,
Wang, X.,
Densely Connected Search Space for More Flexible Neural Architecture
Search,
CVPR20(10625-10634)
IEEE DOI
2008
Routing, Tensile stress, Estimation,
Approximation algorithms, Spatial resolution, Microprocessors
BibRef
Li, Y.,
Jin, X.,
Mei, J.,
Lian, X.,
Yang, L.,
Xie, C.,
Yu, Q.,
Zhou, Y.,
Bai, S.,
Yuille, A.L.,
Neural Architecture Search for Lightweight Non-Local Networks,
CVPR20(10294-10303)
IEEE DOI
2008
Neural networks, Computational modeling,
Task analysis, Graphics processing units, Mobile handsets,
Computational complexity
BibRef
Hu, S.K.[Shou-Kang],
Xie, S.R.[Si-Rui],
Zheng, H.H.[He-Hui],
Liu, C.X.[Chun-Xiao],
Shi, J.P.[Jian-Ping],
Liu, X.Y.[Xun-Ying],
Lin, D.H.[Da-Hua],
DSNAS: Direct Neural Architecture Search Without Parameter Retraining,
CVPR20(12081-12089)
IEEE DOI
2008
Task analysis, Optimization, Training,
Search problems, Measurement, Machine learning
BibRef
Atzmon, M.[Matan],
Nagano, K.[Koki],
Fidler, S.[Sanja],
Khamis, S.[Sameh],
Lipman, Y.[Yaron],
Frame Averaging for Equivariant Shape Space Learning,
CVPR22(621-631)
IEEE DOI
2210
Training, Representation learning, Shape, Neural networks,
Decoding, Pattern recognition, Representation learning
BibRef
Atzmon, M.[Matan],
Lipman, Y.[Yaron],
SAL: Sign Agnostic Learning of Shapes From Raw Data,
CVPR20(2562-2571)
IEEE DOI
2008
Surface reconstruction, Shape,
Neural networks, Interpolation, Mathematical model, Training
BibRef
Li, Z.,
Xi, T.,
Deng, J.,
Zhang, G.,
Wen, S.,
He, R.,
GP-NAS: Gaussian Process Based Neural Architecture Search,
CVPR20(11930-11939)
IEEE DOI
2008
Correlation, Kernel, Training, Task analysis,
Network architecture, Mutual information
BibRef
He, C.,
Ye, H.,
Shen, L.,
Zhang, T.,
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level
Reformulation,
CVPR20(11990-11999)
IEEE DOI
2008
Mathematical model, Training,
Optimization methods, Training data, Neural networks
BibRef
Tao, Y.,
Ma, R.,
Shyu, M.,
Chen, S.,
Challenges in Energy-Efficient Deep Neural Network Training with FPGA,
LPCV20(1602-1611)
IEEE DOI
2008
Field programmable gate arrays, Computational modeling, Training,
Hardware, Neural networks, Machine learning, Graphics processing units
BibRef
Liu, P.,
Wu, B.,
Ma, H.,
Seok, M.,
MemNAS: Memory-Efficient Neural Architecture Search With Grow-Trim
Learning,
CVPR20(2105-2113)
IEEE DOI
2008
Memory management, Neural networks, Correlation,
Performance evaluation, Computational modeling
BibRef
Gao, C.[Chen],
Chen, Y.P.[Yun-Peng],
Liu, S.[Si],
Tan, Z.X.[Zhen-Xiong],
Yan, S.C.[Shui-Cheng],
AdversarialNAS: Adversarial Neural Architecture Search for GANs,
CVPR20(5679-5688)
IEEE DOI
2008
Generators, Task analysis,
Convolution, Generative adversarial networks
BibRef
Li, G.H.[Guo-Hao],
Qian, G.C.[Guo-Cheng],
Delgadillo, I.C.[Itzel C.],
Müller, M.[Matthias],
Thabet, A.[Ali],
Ghanem, B.[Bernard],
SGAS: Sequential Greedy Architecture Search,
CVPR20(1617-1627)
IEEE DOI
2008
Neural Architecture Search.
Correlation, Search problems, Task analysis,
Optimization, Computational efficiency, Microprocessors
BibRef
Chen, X.[Xin],
Xie, L.X.[Ling-Xi],
Wu, J.[Jun],
Tian, Q.[Qi],
Progressive Differentiable Architecture Search:
Bridging the Depth Gap Between Search and Evaluation,
ICCV19(1294-1303)
IEEE DOI
2004
Code, Search.
WWW Link. approximation theory, image recognition,
learning (artificial intelligence), neural net architecture,
Computational modeling
BibRef
Banerjee, S.,
Chakraborty, S.,
Deepsub: A Novel Subset Selection Framework for Training Deep
Learning Architectures,
ICIP19(1615-1619)
IEEE DOI
1910
Submodular optimization, Deep learning
BibRef
Xiong, Y.,
Mehta, R.,
Singh, V.,
Resource Constrained Neural Network Architecture Search:
Will a Submodularity Assumption Help?,
ICCV19(1901-1910)
IEEE DOI
2004
learning (artificial intelligence), neural nets, optimisation,
neural network architecture search, empirical feedback,
Heuristic algorithms
BibRef
Zhao, R.,
Luk, W.,
Efficient Structured Pruning and Architecture Searching for Group
Convolution,
NeruArch19(1961-1970)
IEEE DOI
2004
convolutional neural nets, group theory, network theory (graphs),
neural net architecture, search problems, network pruning,
efficient inference
BibRef
Li, X.[Xin],
Zhou, Y.M.[Yi-Ming],
Pan, Z.[Zheng],
Feng, J.S.[Jia-Shi],
Partial Order Pruning: For Best Speed/Accuracy Trade-Off in Neural
Architecture Search,
CVPR19(9137-9145).
IEEE DOI
2002
BibRef
Guo, M.H.[Ming-Hao],
Zhong, Z.[Zhao],
Wu, W.[Wei],
Lin, D.[Dahua],
Yan, J.J.[Jun-Jie],
IRLAS: Inverse Reinforcement Learning for Architecture Search,
CVPR19(9013-9021).
IEEE DOI
2002
search network structures that are topologically inspired by
human-designed network
BibRef
Gong, X.Y.[Xin-Yu],
Chang, S.Y.[Shi-Yu],
Jiang, Y.F.[Yi-Fan],
Wang, Z.Y.[Zhang-Yang],
AutoGAN: Neural Architecture Search for Generative Adversarial
Networks,
ICCV19(3223-3233)
IEEE DOI
2004
Code, Generative Adversarial Network.
WWW Link. image classification, image segmentation, neural nets,
neural architecture search, generative adversarial networks,
Prediction algorithms
BibRef
Yan, S.,
Fang, B.,
Zhang, F.,
Zheng, Y.,
Zeng, X.,
Zhang, M.,
Xu, H.,
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking,
NeruArch19(1942-1950)
IEEE DOI
2004
Code, Neural Netowrks.
WWW Link. learning (artificial intelligence), neural net architecture,
multilevel architecture, flexible network architectures,
Hierarchical Masking
BibRef
Wu, B.C.[Bi-Chen],
Dai, X.L.[Xiao-Liang],
Zhang, P.Z.[Pei-Zhao],
Wang, Y.H.[Yang-Han],
Sun, F.[Fei],
Wu, Y.M.[Yi-Ming],
Tian, Y.D.[Yuan-Dong],
Vajda, P.[Peter],
Jia, Y.Q.[Yang-Qing],
Keutzer, K.[Kurt],
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable
Neural Architecture Search,
CVPR19(10726-10734).
IEEE DOI
2002
BibRef
Cui, J.,
Chen, P.,
Li, R.,
Liu, S.,
Shen, X.,
Jia, J.,
Fast and Practical Neural Architecture Search,
ICCV19(6508-6517)
IEEE DOI
2004
learning (artificial intelligence), neural nets, FPNAS,
search process, bi-level optimization problem, design networks,
Network architecture
BibRef
Bashivan, P.[Pouya],
Tensen, M.[Mark],
Dicarlo, J.[James],
Teacher Guided Architecture Search,
ICCV19(5319-5328)
IEEE DOI
2004
convolutional neural nets,
learning (artificial intelligence), neural net architecture,
Network architecture
BibRef
Zheng, X.,
Ji, R.,
Tang, L.,
Zhang, B.,
Liu, J.,
Tian, Q.,
Multinomial Distribution Learning for Effective Neural Architecture
Search,
ICCV19(1304-1313)
IEEE DOI
2004
Code, Neural Networks.
WWW Link. graphics processing units,
learning (artificial intelligence), neural nets,
Search problems
BibRef
Zhu, H.,
An, Z.,
Yang, C.,
Xu, K.,
Zhao, E.,
Xu, Y.,
EENA: Efficient Evolution of Neural Architecture,
NeruArch19(1891-1899)
IEEE DOI
2004
learning (artificial intelligence), neural net architecture,
search problems, crossover operations, evolution process,
guidance of experience gained
BibRef
Tan, M.X.[Ming-Xing],
Chen, B.[Bo],
Pang, R.[Ruoming],
Vasudevan, V.[Vijay],
Sandler, M.[Mark],
Howard, A.[Andrew],
Le, Q.V.[Quoc V.],
MnasNet: Platform-Aware Neural Architecture Search for Mobile,
CVPR19(2815-2823).
IEEE DOI
2002
BibRef
Dong, X.Y.[Xuan-Yi],
Yang, Y.[Yi],
Searching for a Robust Neural Architecture in Four GPU Hours,
CVPR19(1761-1770).
IEEE DOI
2002
BibRef
Dong, X.Y.[Xuan-Yi],
Yang, Y.[Yi],
One-Shot Neural Architecture Search via Self-Evaluated Template
Network,
ICCV19(3680-3689)
IEEE DOI
2004
image sampling, knowledge based systems,
learning (artificial intelligence), neural net architecture,
BibRef
Rashwan, A.,
Kalra, A.,
Poupart, P.,
Matrix Nets: A New Deep Architecture for Object Detection,
NeruArch19(2025-2028)
IEEE DOI
2004
learning (artificial intelligence), neural net architecture,
object detection, Matrix Nets, deep architecture, object detection,
neural architecture
BibRef
Cheng, H.,
Zhang, T.,
Yang, Y.,
Yan, F.,
Teague, H.,
Chen, Y.,
Li, H.,
MSNet: Structural Wired Neural Architecture Search for Internet of
Things,
NeruArch19(2033-2036)
IEEE DOI
2004
convolutional neural nets, Internet of Things,
learning (artificial intelligence), mobile computing,
neural architecture search
BibRef
Chen, H.T.[Han-Ting],
Wang, Y.H.[Yun-He],
Xu, C.[Chang],
Yang, Z.H.[Zhao-Hui],
Liu, C.J.[Chuan-Jian],
Shi, B.X.[Bo-Xin],
Xu, C.J.[Chun-Jing],
Xu, C.[Chao],
Tian, Q.[Qi],
Data-Free Learning of Student Networks,
ICCV19(3513-3521)
IEEE DOI
2004
convolutional neural nets,
learning (artificial intelligence), neural net architecture,
Knowledge engineering
BibRef
Liu, C.X.[Chen-Xi],
Zoph, B.[Barret],
Neumann, M.[Maxim],
Shlens, J.[Jonathon],
Hua, W.[Wei],
Li, L.J.[Li-Jia],
Fei-Fei, L.[Li],
Yuille, A.L.[Alan L.],
Huang, J.[Jonathan],
Murphy, K.[Kevin],
Progressive Neural Architecture Search,
ECCV18(I: 19-35).
Springer DOI
1810
New method for learning CNN
BibRef
Srinivas, S.[Suraj],
Babu, V.[Venkatesh],
Learning Neural Network Architectures using Backpropagation,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Hasegawa, R.[Ryoma],
Hotta, K.[Kazuhiro],
PLSNet: A simple network using Partial Least Squares regression for
image classification,
ICPR16(1601-1606)
IEEE DOI
1705
Convolution, Databases, Feature extraction, Image classification,
Network architecture, Principal component analysis, Training,
Convolutional Neural Network, Deep Learning, PCANet, PLSNet,
Partial Least Squares Regression, Stacked, PLS
BibRef
Elliman, D.G.[David G.],
Youssef, S.M.[Sherin M.],
Contextual Swarm-Based Multi-layered Lattices:
A New Architecture for Contextual Pattern Recognition,
DAS04(496-507).
Springer DOI
0505
BibRef
Yang, Z.H.[Zhao-Hui],
Wang, Y.H.[Yun-He],
Chen, X.H.[Xing-Hao],
Shi, B.X.[Bo-Xin],
Xu, C.[Chao],
Xu, C.J.[Chun-Jing],
Tian, Q.[Qi],
Xu, C.[Chang],
CARS: Continuous Evolution for Efficient Neural Architecture Search,
CVPR20(1826-1835)
IEEE DOI
2008
Optimization, Nickel, Network architecture,
Sorting, Training, Automobiles
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks for Classification and Pattern Recognition .