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Sparse representation, Orthogonal Matching Pursuit,
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2202
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2206
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2212
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2302
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2303
Convergence, Artificial neural networks, Imaging, Acceleration,
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2306
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Earlier: A1, A2, A4, A6, A5, Only:
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2310
Representation learning, Role discovery, Heterogeneous network,
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2311
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2312
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2402
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ICIP21(319-323)
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2201
Image color analysis, Algebra, Quaternions, Neural networks, MIMICs,
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2404
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Earlier: A1, A3, Only:
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IEEE DOI
2210
Training, Visualization, Simultaneous localization and mapping,
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2309
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2309
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2211
ReLU
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Wang, Z.Y.[Zhang-Yang],
Unified Implicit Neural Stylization,
ECCV22(XV:636-654).
Springer DOI
2211
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Chen, Y.[Yinbo],
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ECCV22(XVII:170-187).
Springer DOI
2211
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Saragadam, V.[Vishwanath],
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MINER: Multiscale Implicit Neural Representation,
ECCV22(XXIII:318-333).
Springer DOI
2211
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Strümpler, Y.[Yannick],
Postels, J.[Janis],
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Tombari, F.[Federico],
Implicit Neural Representations for Image Compression,
ECCV22(XXVI:74-91).
Springer DOI
2211
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Zhou, Z.X.[Zi-Xuan],
Ning, X.F.[Xue-Fei],
Cai, Y.[Yi],
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Deng, Y.P.[Yi-Ping],
Dong, Y.H.[Yu-Han],
Yang, H.Z.[Hua-Zhong],
Wang, Y.[Yu],
CLOSE: Curriculum Learning on the Sharing Extent Towards Better
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ECCV22(XX:578-594).
Springer DOI
2211
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Lee, J.[Janghyeon],
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On the Angular Update and Hyperparameter Tuning of a Scale-Invariant
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ECCV22(XII:121-136).
Springer DOI
2211
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Dutson, M.[Matthew],
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Event Neural Networks,
ECCV22(XI:276-293).
Springer DOI
2211
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PEA: Improving the Performance of ReLU Networks for Free by Using
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ECV22(2797-2805)
IEEE DOI
2210
Training, Image segmentation, Semantics,
Neural networks, Network architecture
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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
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Lin, F.Q.[Fan-Qing],
Price, B.[Brian],
Martinez, T.[Tony],
Generalizing Interactive Backpropagating Refinement for Dense
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CVPR22(763-772)
IEEE DOI
2210
Deep learning, Image segmentation, Visualization, Shape, Semantics,
Estimation, Deep learning architectures and techniques,
Vision applications and systems
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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
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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, 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
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
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
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.W.[Zi-Wei],
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Architecture, Neural Architecture Search, NAS .