Ghesu, F.C.[Florin C.],
Georgescu, B.,
Zheng, Y.,
Grbic, S.,
Maier, A.,
Hornegger, J.[Joachim],
Comaniciu, D.,
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark
Detection in CT Scans,
PAMI(41), No. 1, January 2019, pp. 176-189.
IEEE DOI
1812
Machine learning, Biomedical imaging, Search problems, Training,
Real-time systems, Deep learning,
intelligent localization
BibRef
Wu, F.[Fei],
Wang, Z.H.[Zhu-Hao],
Lu, W.M.[Wei-Ming],
Li, X.[Xi],
Yang, Y.[Yi],
Luo, J.B.[Jie-Bo],
Zhuang, Y.T.[Yue-Ting],
Regularized Deep Belief Network for Image Attribute Detection,
CirSysVideo(27), No. 7, July 2017, pp. 1464-1477.
IEEE DOI
1707
Computational modeling, Context modeling, Correlation,
Feature extraction, Neural networks, Semantics, Training,
Contextual correlation, deep belief network (DBN), deep learning,
image, attribute
BibRef
Lee, H.,
Kwon, H.,
Going Deeper With Contextual CNN for Hyperspectral Image
Classification,
IP(26), No. 10, October 2017, pp. 4843-4855.
IEEE DOI
1708
geophysical image processing, hyperspectral imaging,
image classification, neural nets,
CNN-based hyperspectral image classification,
contextual deep CNN, joint spatio-spectral feature map,
local contextual interactions,
multiscale convolutional filter bank,
Biological neural networks, Feature extraction,
Hyperspectral imaging, Principal component analysis, Training,
hyperspectral image classification, multi-scale filter bank,
BibRef
Wang, K.[Keze],
Zhang, D.Y.[Dong-Yu],
Li, Y.[Ya],
Zhang, R.M.[Rui-Mao],
Lin, L.[Liang],
Cost-Effective Active Learning for Deep Image Classification,
CirSysVideo(27), No. 12, December 2017, pp. 2591-2600.
IEEE DOI
1712
Labeling, Learning systems, Measurement uncertainty,
Neural networks, Training, Uncertainty, Visualization,
incremental learning
BibRef
Liu, Y.F.[Yan-Fei],
Zhong, Y.F.[Yan-Fei],
Fei, F.[Feng],
Zhu, Q.Q.[Qi-Qi],
Qin, Q.Q.[Qian-Qing],
Scene Classification Based on a Deep Random-Scale Stretched
Convolutional Neural Network,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Liu, Y.F.[Yan-Fei],
Zhong, Y.F.[Yan-Fei],
Qin, Q.Q.[Qian-Qing],
Scene Classification Based on Multiscale Convolutional Neural Network,
GeoRS(56), No. 12, December 2018, pp. 7109-7121.
IEEE DOI
1812
Feature extraction, Remote sensing, Semantics,
Convolutional neural networks, Training, Machine learning,
similarity measure
BibRef
Zhou, Y.C.[Yu-Can],
Hu, Q.H.[Qing-Hua],
Wang, Y.[Yu],
Deep super-class learning for long-tail distributed image
classification,
PR(80), 2018, pp. 118-128.
Elsevier DOI
1805
Super-class construction, Block-structured sparsity,
Deep learning, Long-tail distribution
BibRef
Zheng, X.T.[Xiang-Tao],
Yuan, Y.[Yuan],
Lu, X.Q.[Xiao-Qiang],
A Deep Scene Representation for Aerial Scene Classification,
GeoRS(57), No. 7, July 2019, pp. 4799-4809.
IEEE DOI
1907
Feature extraction, Encoding, Strain, Task analysis, Remote sensing,
Training, Semantics, Aerial scene classification,
multiscale representation
BibRef
Liao, Z.B.[Zhi-Bin],
Drummond, T.[Tom],
Reid, I.D.[Ian D.],
Carneiro, G.[Gustavo],
Approximate Fisher Information Matrix to Characterize the Training of
Deep Neural Networks,
PAMI(42), No. 1, January 2020, pp. 15-26.
IEEE DOI
1912
Training, Machine learning, Neural networks,
Computational modeling, Convergence, Linear programming, Testing,
neural network training characterisation
BibRef
Liao, Z.B.[Zhi-Bin],
Carneiro, G.[Gustavo],
On the importance of normalisation layers in deep learning with
piecewise linear activation units,
WACV16(1-8)
IEEE DOI
1606
Data models
BibRef
Zhuang, B.,
Lin, G.,
Shen, C.,
Reid, I.D.,
Fast Training of Triplet-Based Deep Binary Embedding Networks,
CVPR16(5955-5964)
IEEE DOI
1612
BibRef
Lyu, K.,
Li, Y.,
Zhang, Z.,
Attention-Aware Multi-Task Convolutional Neural Networks,
IP(29), No. 1, 2020, pp. 1867-1878.
IEEE DOI
1912
Task analysis, Deep learning, Feature extraction, Training,
Estimation, Semantics, Convolutional neural networks,
representation sharing
BibRef
Zhang, D.W.[Ding-Wen],
Han, J.W.[Jun-Wei],
Zhang, Y.[Yu],
Xu, D.[Dong],
Synthesizing Supervision for Learning Deep Saliency Network without
Human Annotation,
PAMI(42), No. 7, July 2020, pp. 1755-1769.
IEEE DOI
2006
Object detection, Detectors, Training, Knowledge engineering,
Task analysis, Semantics, Feature extraction,
weakly supervised semantic segmentation
BibRef
Santiago, C.[Carlos],
Barata, C.[Catarina],
Sasdelli, M.[Michele],
Carneiro, G.[Gustavo],
Nascimento, J.C.[Jacinto C.],
LOW: Training deep neural networks by learning optimal sample weights,
PR(110), 2021, pp. 107585.
Elsevier DOI
2011
Deep learning, Sample weighting, Imbalanced data sets
BibRef
Dixit, M.[Mandar],
Li, Y.S.[Yun-Sheng],
Vasconcelos, N.M.[Nuno M.],
Semantic Fisher Scores for Task Transfer:
Using Objects to Classify Scenes,
PAMI(42), No. 12, December 2020, pp. 3102-3118.
IEEE DOI
2011
BibRef
And: A2, A1, A3:
Deep Scene Image Classification with the MFAFVNet,
ICCV17(5757-5765)
IEEE DOI
1802
Semantics, Neural networks, Training data, Object recognition,
Neural networks, Computational modeling, Probability, MFA.
Computational modeling, Covariance matrices,
Feature extraction, Training
BibRef
Luo, P.[Ping],
Zhang, R.M.[Rui-Mao],
Ren, J.M.[Jia-Min],
Peng, Z.L.[Zhang-Lin],
Li, J.Y.[Jing-Yu],
Switchable Normalization for Learning-to-Normalize Deep
Representation,
PAMI(43), No. 2, February 2021, pp. 712-728.
IEEE DOI
2101
Task analysis, Training, Graphics processing units, Switches,
Object detection, Image segmentation, Head, Deep learning,
semantic segmentation and face verification
BibRef
Liu, D.[Defu],
Ning, J.[Jin],
Wu, J.Z.[Jin-Zhao],
Yang, G.W.[Guo-Wu],
Extending Ordinary-Label Learning Losses to Complementary-Label
Learning,
SPLetters(28), 2021, pp. 852-856.
IEEE DOI
2106
Training, Neural networks, Signal processing algorithms,
Risk management, Mean square error methods, Machine learning,
deep neural networks
BibRef
Chen, Z.[Zhe],
Wu, X.J.[Xiao-Jun],
Xu, T.Y.[Tian-Yang],
Kittler, J.V.[Josef V.],
Learning Alternating Deep-Layer Cascaded Representation,
SPLetters(28), 2021, pp. 1520-1524.
IEEE DOI
2108
Training, Encoding, Collaboration, Mathematical model,
Feature extraction, Deep learning,
image classification
BibRef
Wang, R.[Rui],
Wu, X.J.[Xiao-Jun],
Chen, Z.H.[Zi-Heng],
Xu, T.Y.[Tian-Yang],
Kittler, J.V.[Josef V.],
DreamNet: A Deep Riemannian Manifold Network for SPD Matrix Learning,
ACCV22(VI:646-663).
Springer DOI
2307
symmetric positive definite.
BibRef
Fan, B.[Bin],
Liu, H.M.[Hong-Min],
Zeng, H.[Hui],
Zhang, J.Y.[Ji-Yong],
Liu, X.[Xin],
Han, J.W.[Jun-Wei],
Deep Unsupervised Binary Descriptor Learning Through Locality
Consistency and Self Distinctiveness,
MultMed(23), 2021, pp. 2770-2781.
IEEE DOI
2109
Machine learning, Robustness, Quantization (signal), Binary codes,
Task analysis, Feature extraction, Training, Unsupervised learning,
image retrieval
BibRef
Miao, Y.Q.[Yun-Qi],
Lin, Z.J.[Zi-Jia],
Ma, X.[Xiao],
Ding, G.G.[Gui-Guang],
Han, J.G.[Jun-Gong],
Learning Transformation-Invariant Local Descriptors With Low-Coupling
Binary Codes,
IP(30), 2021, pp. 7554-7566.
IEEE DOI
2109
Correlation, Binary codes, Training, Visualization,
Feature extraction, Entropy, Deep learning, deep learning
BibRef
Zhao, T.Y.[Tian-Yu],
Zhao, J.[Jian],
Zhou, W.G.[Wen-Gang],
Zhou, Y.[Yun],
Li, H.Q.[Hou-Qiang],
State Representation Learning With Adjacent State Consistency Loss
for Deep Reinforcement Learning,
MultMedMag(28), No. 3, July 2021, pp. 117-127.
IEEE DOI
2109
Training data, Task analysis, Reinforcement learning,
Feature extraction, Games, Neural networks, Deep learning
BibRef
Lu, Z.Q.[Zi-Qing],
Xu, C.[Chang],
Du, B.[Bo],
Ishida, T.[Takashi],
Zhang, L.[Lefei],
Sugiyama, M.[Masashi],
LocalDrop: A Hybrid Regularization for Deep Neural Networks,
PAMI(44), No. 7, July 2022, pp. 3590-3601.
IEEE DOI
2206
Complexity theory, Biological neural networks, Bayes methods,
Training, Deep learning, Upper bound, Random variables,
regularization
BibRef
Wang, Y.L.[Yu-Lin],
Huang, G.[Gao],
Song, S.[Shiji],
Pan, X.[Xuran],
Xia, Y.T.[Yi-Tong],
Wu, C.[Cheng],
Regularizing Deep Networks With Semantic Data Augmentation,
PAMI(44), No. 7, July 2022, pp. 3733-3748.
IEEE DOI
2206
Semantics, Training, Task analysis, Upper bound, Training data,
Data models, Supervised learning, Data augmentation, deep learning,
semi-supervised learning
BibRef
Pan, X.[Xuran],
Ge, C.J.[Chun-Jiang],
Lu, R.[Rui],
Song, S.[Shiji],
Chen, G.F.[Guan-Fu],
Huang, Z.Y.[Ze-Yi],
Huang, G.[Gao],
On the Integration of Self-Attention and Convolution,
CVPR22(805-815)
IEEE DOI
2210
Code, Representation Learning.
WWW Link. Representation learning, Image recognition, Convolution,
Computational modeling, Object detection, Pattern recognition,
Representation learning
BibRef
Kim, J.[Jinwook],
Yoon, H.[Heeyong],
Kim, M.S.[Min-Soo],
Tweaking Deep Neural Networks,
PAMI(44), No. 9, September 2022, pp. 5715-5728.
IEEE DOI
2208
Synapses, Biological neural networks, Neurons,
Artificial neural networks, Training, Training data, Arrays,
synaptic join
BibRef
Li, Y.[Yao],
Wang, Y.H.[Yu-Hui],
Gan, Y.Z.[Yao-Zhong],
Tan, X.Y.[Xiao-Yang],
Alleviating the estimation bias of deep deterministic policy gradient
via co-regularization,
PR(131), 2022, pp. 108872.
x
Elsevier DOI
2208
Reinforcement learning, Overestimation, Underestimation,
Co-training, Deterministic policy gradient
BibRef
Li, Y.[Yao],
Wang, Y.H.[Yu-Hui],
Tan, X.Y.[Xiao-Yang],
Self-imitation guided goal-conditioned reinforcement learning,
PR(144), 2023, pp. 109845.
Elsevier DOI
2310
Goal-conditioned reinforcement learning,
Self-imitation learning, Deterministic policy gradient, Behavior cloning
BibRef
Wang, D.[Di],
Tang, L.[Lulu],
Wang, X.[Xu],
Luo, L.Q.[Lu-Qing],
Yang, Z.X.[Zhi-Xin],
Improving deep learning on point cloud by maximizing mutual
information across layers,
PR(131), 2022, pp. 108892.
Elsevier DOI
2208
Deep learning, 3D vision, Point clouds, Mutual information
BibRef
Lei, C.Y.[Chen-Yang],
Xing, Y.Z.[Ya-Zhou],
Ouyang, H.[Hao],
Chen, Q.F.[Qi-Feng],
Deep Video Prior for Video Consistency and Propagation,
PAMI(45), No. 1, January 2023, pp. 356-371.
IEEE DOI
2212
WWW Link. Consistent learning across the video.
Task analysis, Training, Electronics packaging,
Image color analysis, Optical imaging, Image reconstruction, deep learning
BibRef
Murdock, C.[Calvin],
Cazenavette, G.[George],
Lucey, S.[Simon],
Reframing Neural Networks:
Deep Structure in Overcomplete Representations,
PAMI(45), No. 1, January 2023, pp. 964-979.
IEEE DOI
2212
Neural networks, Deep learning, Computer architecture,
Representation learning, Optimization, Network architecture, sparsity
BibRef
Wang, Z.W.[Zi-Wei],
Xiao, H.[Han],
Duan, Y.Q.[Yue-Qi],
Zhou, J.[Jie],
Lu, J.W.[Ji-Wen],
Learning Deep Binary Descriptors via Bitwise Interaction Mining,
PAMI(45), No. 2, February 2023, pp. 1919-1933.
IEEE DOI
2301
Reliability, Reinforcement learning, Costs, Binary codes, Training,
Task analysis, Semantics, Binary descriptors, graph convolutional networks
BibRef
Duan, Y.Q.[Yue-Qi],
Wang, Z.W.[Zi-Wei],
Lu, J.W.[Ji-Wen],
Lin, X.D.[Xu-Dong],
Zhou, J.[Jie],
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning,
CVPR18(8270-8279)
IEEE DOI
1812
Reliability, Binary codes, Linear programming,
Training, Mutual information
BibRef
Chen, D.D.[Dong-Dong],
Davies, M.[Mike],
Ehrhardt, M.J.[Matthias J.],
Schönlieb, C.B.[Carola-Bibiane],
Sherry, F.[Ferdia],
Tachella, J.[Julián],
Imaging With Equivariant Deep Learning:
From unrolled network design to fully unsupervised learning,
SPMag(40), No. 1, January 2023, pp. 134-147.
IEEE DOI
2301
Deep learning, Neural networks, Imaging, Signal processing algorithms,
Self-supervised learning, Iterative methods
BibRef
Tang, A.[Anda],
Niu, L.F.[Ling-Feng],
Miao, J.Y.[Jian-Yu],
Zhang, P.[Peng],
Training Compact DNNs with L1/2 Regularization,
PR(136), 2023, pp. 109206.
Elsevier DOI
2301
Deep neural networks, Model compression, Quasi-norm,
Non-Lipschitz regularization, Sparse optimization
BibRef
Shu, J.[Jun],
Zhu, Y.[Yanwen],
Zhao, Q.[Qian],
Meng, D.Y.[De-Yu],
Xu, Z.B.[Zong-Ben],
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks,
PAMI(45), No. 3, March 2023, pp. 3505-3521.
IEEE DOI
2302
WWW Link. LR: Learning Rate.
Schedules, Task analysis, Training, Optimization, Convergence, Data models,
Heuristic algorithms, Meta learning, stochastic gradient descent
BibRef
Wang, Y.M.[Yi-Ming],
Chang, D.X.[Dong-Xia],
Fu, Z.Q.[Zhi-Qiang],
Zhao, Y.[Yao],
Learning a bi-directional discriminative representation for deep
clustering,
PR(137), 2023, pp. 109237.
Elsevier DOI
2302
Deep clustering, Representation learning, Manifold learning, Mutual information
BibRef
Zhu, Q.Y.[Qiu-Yu],
Zu, X.W.[Xue-Wen],
A Softmax-Free Loss Function Based on Predefined Optimal-Distribution
of Latent Features for Deep Learning Classifier,
CirSysVideo(33), No. 3, March 2023, pp. 1386-1397.
IEEE DOI
2303
Training, Feature extraction, Deep learning, Neural networks,
Image classification, Face recognition
BibRef
Qin, W.[Wei],
Zhang, H.W.[Han-Wang],
Hong, R.C.[Ri-Chang],
Lim, E.P.[Ee-Peng],
Sun, Q.R.[Qian-Ru],
Causal Interventional Training for Image Recognition,
MultMed(25), 2023, pp. 1033-1044.
IEEE DOI
2303
WWW Link. Training without bias.
Training, Automobiles, Task analysis, Visualization,
Road transportation, Image recognition, Data models, Causality,
image recognition
BibRef
Chen, C.[Cheng],
Li, B.[Bo],
An Interpretable Channelwise Attention Mechanism based on Asymmetric
and Skewed Gaussian Distribution,
PR(139), 2023, pp. 109467.
Elsevier DOI
2304
Channelwise attention, Interpretable modeling,
Skewness distribution, Asymmetric distribution
BibRef
Wu, Z.Z.[Zi-Zhang],
Wang, M.[Man],
Sun, W.W.[Wei-Wei],
Li, Y.C.[Yu-Chen],
Xu, T.H.[Tian-Hao],
Wang, F.[Fan],
Huang, K.[Keke],
CAT: Learning to collaborate channel and spatial attention from
multi-information fusion,
IET-CV(17), No. 3, 2023, pp. 309-318.
DOI Link
2305
Performance boost for deep learning.
channel attention, dynamic learning, entropy pooling, spatial attention
BibRef
Huang, Z.Z.[Zhi-Zhong],
Chen, J.[Jie],
Zhang, J.P.[Jun-Ping],
Shan, H.M.[Hong-Ming],
Learning Representation for Clustering Via Prototype Scattering and
Positive Sampling,
PAMI(45), No. 6, June 2023, pp. 7509-7524.
IEEE DOI
WWW Link.
2305
Prototypes, Scattering, Representation learning, Task analysis,
Self-supervised learning, Clustering methods, Semantics,
unsupervised learning
BibRef
Feng, F.[Fan],
Liu, Q.[Qi],
Peng, Z.L.[Zhang-Lin],
Zhang, R.M.[Rui-Mao],
Chan, R.H.M.[Rosa H.M.],
Community Channel-Net: Efficient channel-wise interactions via
community graph topology,
PR(141), 2023, pp. 109536.
Elsevier DOI
2306
Deep Neural Networks, Complex Networks, Representation Learning
BibRef
Xu, Y.H.[Yu-Hui],
Xie, L.X.[Ling-Xi],
Xie, C.H.[Ci-Hang],
Dai, W.R.[Wen-Rui],
Mei, J.[Jieru],
Qiao, S.Y.[Si-Yuan],
Shen, W.[Wei],
Xiong, H.K.[Hong-Kai],
Yuille, A.L.[Alan L.],
BNET: Batch Normalization With Enhanced Linear Transformation,
PAMI(45), No. 7, July 2023, pp. 9225-9232.
IEEE DOI
2306
Task analysis, Convolution, Training, Visualization, Neurons, Kernel,
Performance gain, Batch normalization, linear transformation, deep learning
BibRef
Huang, Y.J.[Yu-Jie],
Chen, W.S.[Wen-Shu],
Peng, L.Y.[Li-Yuan],
Liu, Y.H.[Yu-Hao],
Wang, M.Y.[Ming-Yu],
Zhang, X.P.[Xiao-Ping],
Zeng, X.Y.[Xiao-Yang],
LineDL: Processing Images Line-by-Line With Deep Learning,
IP(32), 2023, pp. 3150-3162.
IEEE DOI
2306
Task analysis, Image processing, Noise reduction,
Computational modeling, Image coding, Superresolution, superresolution
BibRef
Lin, M.B.[Ming-Bao],
Chen, B.H.[Bo-Hong],
Chao, F.[Fei],
Ji, R.R.[Rong-Rong],
Training Compact CNNs for Image Classification Using Dynamic-Coded
Filter Fusion,
PAMI(45), No. 8, August 2023, pp. 10478-10487.
IEEE DOI
2307
Training, Convolutional neural networks, Computational modeling,
Temperature distribution, Information filters, Convolution,
compact CNNs
BibRef
Khan, S.D.[Sultan Daud],
Basalamah, S.[Saleh],
Multi-Branch Deep Learning Framework for Land Scene Classification in
Satellite Imagery,
RS(15), No. 13, 2023, pp. 3408.
DOI Link
2307
BibRef
Zhu, Z.Z.[Ze-Zhou],
Zhou, Y.C.[Yu-Cong],
Dong, Y.[Yuan],
Zhong, Z.[Zhao],
PWLU: Learning Specialized Activation Functions With the Piecewise
Linear Unit,
PAMI(45), No. 10, October 2023, pp. 12269-12286.
IEEE DOI
2310
BibRef
Earlier: A2, A1, A4, Only:
Learning specialized activation functions with the Piecewise Linear
Unit,
ICCV21(12075-12084)
IEEE DOI
2203
Learning systems, Deep learning, Neural networks,
Task analysis, Recognition and classification
BibRef
Chen, X.H.[Xiong-Hui],
Luo, F.M.[Fan-Ming],
Yu, Y.[Yang],
Li, Q.Y.[Qing-Yang],
Qin, Z.W.[Zhi-Wei],
Shang, W.J.[Wen-Jie],
Ye, J.P.[Jie-Ping],
Offline Model-Based Adaptable Policy Learning for Decision-Making in
Out-of-Support Regions,
PAMI(45), No. 12, December 2023, pp. 15260-15274.
IEEE DOI
2311
BibRef
Wang, Q.L.[Qi-Long],
Zhang, Z.L.[Zhao-Lin],
Gao, M.Z.[Ming-Ze],
Xie, J.T.[Jiang-Tao],
Zhu, P.F.[Peng-Fei],
Li, P.H.[Pei-Hua],
Zuo, W.M.[Wang-Meng],
Hu, Q.H.[Qing-Hua],
Towards a Deeper Understanding of Global Covariance Pooling in Deep
Learning: An Optimization Perspective,
PAMI(45), No. 12, December 2023, pp. 15802-15819.
IEEE DOI
2311
BibRef
Wang, J.[Jing],
Chen, J.H.[Jia-Hong],
Zhang, K.[Kuangen],
Sigal, L.[Leonid],
Training feedforward neural nets in Hopfield-energy-based
configuration: A two-step approach,
PR(145), 2024, pp. 109954.
Elsevier DOI
2311
Hopfield-based energy, Feedforward neural nets,
Learning algorithm, Supervised learning
BibRef
Todros, K.[Koby],
Training a Radial Basis Function Network Under Transformed
Probability Measure,
SPLetters(30), 2023, pp. 1567-1571.
IEEE DOI
2311
BibRef
Xing, W.W.[Wei-Wei],
Yao, J.[Jie],
Liu, Z.X.[Zi-Xia],
Liu, W.B.[Wei-Bin],
Zhang, S.[Shunli],
Wang, L.Q.[Li-Qiang],
Contrastive JS: A Novel Scheme for Enhancing the Accuracy and
Robustness of Deep Models,
MultMed(25), 2023, pp. 7881-7893.
IEEE DOI
2312
BibRef
Wang, Y.[Yida],
Tan, D.J.[David Joseph],
Navab, N.[Nassir],
Tombari, F.[Federico],
Self-Supervised Latent Space Optimization With Nebula Variational
Coding,
PAMI(46), No. 3, March 2024, pp. 1397-1411.
IEEE DOI
2402
Variational model to help deep learning.
Training, Measurement, Image reconstruction, Decoding, Optimization,
Feature extraction, Nebula anchor, variational inference,
metric learning
BibRef
Lee, H.[Hyeongmin],
Kim, T.[Taeoh],
Son, H.[Hanbin],
Baek, S.W.[Sang-Wook],
Cheon, M.[Minsu],
Lee, S.Y.[Sang-Youn],
A Nonlinear, Regularized, and Data-Independent Modulation for
Continuously Interactive Image Processing Network,
IJCV(132), No. 1, January 2024, pp. 74-94.
Springer DOI
2402
Usually CNN trained to, e.g. denoise a certain degree of noise, so multiple
training for general noise levels. How to avoid this.
BibRef
Zhao, C.Y.[Chao-Yu],
Qian, J.J.[Jian-Jun],
Zhu, S.M.[Shu-Min],
Xie, J.[Jin],
Yang, J.[Jian],
Learning Robust Facial Representation From the View of Diversity and
Closeness,
IJCV(132), No. 2, February 2024, pp. 410-427.
Springer DOI
2402
Both closeess and diversity in training.
BibRef
Zhou, H.Y.[He-Yu],
Liu, A.A.[An-An],
Zhang, C.Y.[Chen-Yu],
Zhu, P.[Ping],
Zhang, Q.Y.[Qian-Yi],
Kankanhalli, M.[Mohan],
Multi-Modal Meta-Transfer Fusion Network for Few-Shot 3D Model
Classification,
IJCV(132), No. 3, March 2024, pp. 673-688.
Springer DOI
2402
Scaling and Shifting to avoid overtraining.
BibRef
Trosten, D.J.[Daniel J.],
Løkse, S.[Sigurd],
Jenssen, R.[Robert],
Kampffmeyer, M.[Michael],
Leveraging tensor kernels to reduce objective function mismatch in
deep clustering,
PR(149), 2024, pp. 110229.
Elsevier DOI Code:
WWW Link.
2403
Tensor kernels, Unsupervised companion objectives,
Objective function mismatch, Deep clustering
BibRef
Qin, Z.[Zhen],
Tan, X.[Xuwei],
Zhu, Z.H.[Zhi-Hui],
Convergence Analysis for Learning Orthonormal Deep Linear Neural
Networks,
SPLetters(31), 2024, pp. 795-799.
IEEE DOI
2404
Training, Convergence, Neural networks, Manifolds, Covariance matrices,
Robustness, Optimization, Riemannian optimization
BibRef
Liu, X.Z.[Xin-Zhi],
Yu, J.[Jun],
Kurihara, T.[Toru],
Wu, C.Z.[Cong-Zhong],
Zhan, S.[Shu],
Spectrum Attention Mechanism for a Complex Neural Network,
SPLetters(31), 2024, pp. 815-819.
IEEE DOI
2404
Discrete cosine transforms, Neural networks, Discrete Fourier transforms,
Training, Hyperspectral imaging, spectrum attention
BibRef
Hamidi, S.M.[Shayan Mohajer],
Training Neural Networks on Remote Edge Devices for Unseen Class
Classification,
SPLetters(31), 2024, pp. 1004-1008.
IEEE DOI
2404
Training, Vectors, Artificial neural networks, Dogs, Costs, Entropy,
Computational modeling, Training on edge devices,
Lipschitz regularization
BibRef
Yin, Z.[Zimo],
Pu, J.[Jian],
Wan, R.[Ru],
Xue, X.Y.[Xiang-Yang],
Embrace sustainable AI: Dynamic data subset selection for image
classification,
PR(151), 2024, pp. 110392.
Elsevier DOI
2404
Data selection, Dynamic subset selection, Weighted sampling,
Class distribution, Training efficiency
BibRef
Li, J.T.[Jun-Tao],
Liang, X.B.[Xiao-Bo],
Wu, L.J.[Li-Jun],
Wang, Y.[Yue],
Meng, Q.[Qi],
Qin, T.[Tao],
Zhang, M.[Min],
Liu, T.Y.[Tie-Yan],
Randomness Regularization With Simple Consistency Training for Neural
Networks,
PAMI(46), No. 8, August 2024, pp. 5763-5778.
IEEE DOI
2407
Training, Data models, Transformers, Mathematical models,
Task analysis, Predictive models, Computational modeling,
neural networks
BibRef
Zhong, Y.Y.[Yang-Yang],
Yan, Y.F.[Yun-Feng],
Luo, P.X.[Peng-Xin],
Zhou, Y.H.[Yu-Hao],
Qi, D.L.[Dong-Lian],
ADIR: Advanced domain-invariant representation via decoupling
learning and information bottleneck,
IET-IPR(18), No. 9, 2024, pp. 2506-2520.
DOI Link
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Biases in learning DNN/CNN
image processing
BibRef
Barisin, T.[Tin],
Angulo, J.[Jesus],
Schladitz, K.[Katja],
Redenbach, C.[Claudia],
Riesz Feature Representation: Scale Equivariant Scattering Network
for Classification Tasks,
SIIMS(17), No. 2, 2024, pp. 1284-1313.
DOI Link
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BibRef
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Xiao, M.Q.[Ming-Qing],
Fang, C.[Cong],
Lin, Z.C.[Zhou-Chen],
Designing Universally-Approximating Deep Neural Networks:
A First-Order Optimization Approach,
PAMI(46), No. 9, September 2024, pp. 6231-6246.
IEEE DOI
2408
Optimization, Approximation algorithms, Design methodology, Manuals,
Reviews, Knowledge engineering, Image classification, width-bounded
BibRef
Bojesomo, A.[Alabi],
Liatsis, P.[Panos],
Al Marzouqi, H.[Hasan],
Deep Hypercomplex Networks for Spatiotemporal Data Processing:
Parameter efficiency and superior performance,
SPMag(41), No. 3, May 2024, pp. 101-112.
IEEE DOI
2408
[Hypercomplex Signal and Image Processing]
Training data, Convolutional neural networks, Algebra, Quaternions,
Image processing, Data processing, Spatiotemporal phenomena, Batch normalization
BibRef
Lu, M.F.[Ming-Fei],
Xing, L.[Lei],
Chen, B.D.[Ba-Dong],
Measuring generalized divergence for multiple distributions with
application to deep clustering,
PR(157), 2025, pp. 110864.
Elsevier DOI Code:
WWW Link.
2409
Generalized divergence measures, Sample-based estimation,
Jensen-Rényi divergence, Deep clustering
BibRef
Liu, K.J.[Kang-Jun],
Chen, K.[Ke],
Jia, K.[Kui],
Wang, Y.W.[Yao-Wei],
Improving deep representation learning via auxiliary learnable target
coding,
PR(157), 2025, pp. 110938.
Elsevier DOI
2409
Image classification, Representation learning, Target coding
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Wen, J.J.[Jia-Jun],
Kong, H.[Heng],
Lai, Z.H.[Zhi-Hui],
Zhu, Z.J.[Zhi-Jie],
Characteristic discriminative prototype network with detailed
interpretation for classification,
PR(157), 2025, pp. 110901.
Elsevier DOI
2409
Classification, Prototype learning, Deep learning
BibRef
Feng, G.R.[Guo-Rui],
Li, S.[Sheng],
Zhao, J.[Jian],
Wang, Z.[Zheng],
Recent Advances in Deep Learning Model Security,
PRL(185), 2024, pp. 262-263.
Elsevier DOI
2410
BibRef
Karpukhin, I.[Ivan],
Dereka, S.[Stanislav],
Kolesnikov, S.[Sergey],
EXACT: How to train your accuracy,
PRL(185), 2024, pp. 23-30.
Elsevier DOI
2410
Classification, Image recognition, Deep learning,
Training objective, Loss function, Accuracy enhancement, Label noise
BibRef
Ilhan, F.[Fatih],
Chow, K.H.[Ka-Ho],
Hu, S.[Sihao],
Huang, T.S.[Tian-Sheng],
Tekin, S.[Selim],
Wei, W.Q.[Wen-Qi],
Wu, Y.Z.[Yan-Zhao],
Lee, M.J.[Myung-Jin],
Kompella, R.[Ramana],
Latapie, H.[Hugo],
Liu, G.W.[Gao-Wen],
Liu, L.[Ling],
Adaptive Deep Neural Network Inference Optimization with EENet,
WACV24(1362-1371)
IEEE DOI
2404
Terminate early when confident.
Adaptation models, Visualization, Adaptive systems,
Computational modeling, Optimization methods,
Image recognition and understanding
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Bu, H.R.[Hao-Ran],
Deep Clustering Based on Contractive Autoencoder and Self-paced
Learning,
CVIDL23(458-462)
IEEE DOI
2403
Training, Learning systems, Deep learning, Clustering algorithms,
Big Data, Partitioning algorithms, Self-paced learning
BibRef
He, H.[Haoze],
Dube, P.[Parijat],
RCD-SGD: Resource-Constrained Distributed SGD in Heterogeneous
Environment Via Submodular Partitioning,
REDLCV23(1385-1393)
IEEE DOI
2401
BibRef
Wang, S.[Shunxin],
Brune, C.[Christoph],
Veldhuis, R.[Raymond],
Strisciuglio, N.[Nicola],
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut
Learning,
VIPriors23(129-138)
IEEE DOI Code:
WWW Link.
2401
Augmentation so nets don't get stuck on superficial data.
BibRef
Wei, Y.[Yao],
Sun, Y.C.[Yan-Chao],
Zheng, R.J.[Rui-Jie],
Vemprala, S.[Sai],
Bonatti, R.[Rogerio],
Chen, S.[Shuhang],
Madaan, R.[Ratnesh],
Ba, Z.J.[Zhong-Jie],
Kapoor, A.[Ashish],
Ma, S.[Shuang],
Is Imitation All You Need? Generalized Decision-Making with
Dual-Phase Training,
ICCV23(16175-16185)
IEEE DOI Code:
WWW Link.
2401
BibRef
Wang, Y.L.[Yu-Lin],
Yue, Y.[Yang],
Lu, R.[Rui],
Liu, T.J.[Tian-Jiao],
Zhong, Z.[Zhao],
Song, S.[Shiji],
Huang, G.[Gao],
EfficientTrain: Exploring Generalized Curriculum Learning for
Training Visual Backbones,
ICCV23(5829-5841)
IEEE DOI Code:
WWW Link.
2401
BibRef
Zhou, Y.F.[Yi-Fei],
Li, Z.[Zilu],
Shrivastava, A.[Abhinav],
Zhao, H.S.[Heng-Shuang],
Torralba, A.[Antonio],
Tian, T.[Taipeng],
Lim, S.N.[Ser-Nam],
BT2: Backward-compatible Training with Basis Transformation,
ICCV23(11195-11204)
IEEE DOI Code:
WWW Link.
2401
BibRef
Saratchandran, H.[Hemanth],
Chng, S.F.[Shin-Fang],
Ramasinghe, S.[Sameera],
MacDonald, L.[Lachlan],
Lucey, S.[Simon],
Curvature-Aware Training for Coordinate Networks,
ICCV23(13282-13292)
IEEE DOI
2401
BibRef
Ni, Z.L.[Zan-Lin],
Wang, Y.L.[Yu-Lin],
Yu, J.W.[Jiang-Wei],
Jiang, H.[Haojun],
Cao, Y.[Yue],
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Deep Incubation: Training Large Models by Divide-and-Conquering,
ICCV23(17289-17299)
IEEE DOI Code:
WWW Link.
2401
BibRef
Park, H.[Hyekang],
Noh, J.[Jongyoun],
Oh, Y.[Youngmin],
Baek, D.[Donghyeon],
Ham, B.[Bumsub],
ACLS: Adaptive and Conditional Label Smoothing for Network
Calibration,
ICCV23(3913-3922)
IEEE DOI
2401
BibRef
Rao, S.[Sukrut],
Böhle, M.[Moritz],
Parchami-Araghi, A.[Amin],
Schiele, B.[Bernt],
Studying How to Efficiently and Effectively Guide Models with
Explanations,
ICCV23(1922-1933)
IEEE DOI Code:
WWW Link.
2401
Training deep networks right.
BibRef
Liang, T.[Tong],
Davis, J.[Jim],
Inducing Neural Collapse to a Fixed Hierarchy-Aware Frame for
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ICCV23(1443-1452)
IEEE DOI Code:
WWW Link.
2401
BibRef
Noh, J.[Jongyoun],
Park, H.[Hyekang],
Lee, J.[Junghyup],
Ham, B.[Bumsub],
RankMixup: Ranking-Based Mixup Training for Network Calibration,
ICCV23(1358-1368)
IEEE DOI
2401
BibRef
Voetman, R.[Roy],
van Meekeren, A.[Alexander],
Aghaei, M.[Maya],
Dijkstra, K.[Klaas],
Using Diffusion Models for Dataset Generation:
Prompt Engineering vs. Fine-tuning,
CAIP23(I:142-152).
Springer DOI
2312
Lack of training data is the issue.
BibRef
Spatafora, M.A.N.[Maria Ausilia Napoli],
Ortis, A.[Alessandro],
Battiato, S.[Sebastiano],
GLR: Gradient-based Learning Rate Scheduler,
CIAP23(I:269-281).
Springer DOI
2312
BibRef
Liang, K.M.[Kong-Ming],
Wang, X.R.[Xin-Ran],
Wei, T.[Tao],
Chen, W.[Wei],
Ma, Z.Y.[Zhan-Yu],
Guo, J.[Jun],
Attribute Learning with Knowledge Enhanced Partial Annotations,
ICIP23(1715-1719)
IEEE DOI Code:
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2312
BibRef
Quétu, V.[Victor],
Tartaglione, E.[Enzo],
Dodging the Double Descent in Deep Neural Networks,
ICIP23(1625-1629)
IEEE DOI
2312
BibRef
Kleinman, M.[Michael],
Achille, A.[Alessandro],
Soatto, S.[Stefano],
Critical Learning Periods for Multisensory Integration in Deep
Networks,
CVPR23(24296-24305)
IEEE DOI
2309
BibRef
Solodskikh, K.[Kirill],
Kurbanov, A.[Azim],
Aydarkhanov, R.[Ruslan],
Zhelavskaya, I.[Irina],
Parfenov, Y.[Yury],
Song, D.H.[De-Hua],
Lefkimmiatis, S.[Stamatios],
Integral Neural Networks,
CVPR23(16113-16122)
IEEE DOI
2309
BibRef
He, Q.[Qiang],
Su, H.[Huangyuan],
Zhang, J.[Jieyu],
Hou, X.W.[Xin-Wen],
Frustratingly Easy Regularization on Representation Can Boost Deep
Reinforcement Learning,
CVPR23(20215-20225)
IEEE DOI
2309
BibRef
An, J.[Jaeju],
Kim, T.[Taejune],
Ko, D.[Donggeun],
Lee, S.[Sangyup],
Woo, S.S.[Simon S.],
A2: Adaptive Augmentation for Effectively Mitigating Dataset Bias,
ACCV22(VII:696-712).
Springer DOI
2307
WWW Link. Deep nets overfit the data.
BibRef
Fang, Z.[Zilin],
Shahbazi, M.[Mohamad],
Probst, T.[Thomas],
Paudel, D.P.[Danda Pani],
Van Gool, L.J.[Luc J.],
Training Dynamics Aware Neural Network Optimization with Stabilization,
ACCV22(I:635-651).
Springer DOI
2307
BibRef
Gottlieb, N.[Noam],
Werman, M.[Michael],
Decisionet: A Binary-tree Structured Neural Network,
ACCV22(I:556-570).
Springer DOI
2307
Combined with decision tree concepts.
BibRef
Zhao, Y.[Yang],
Zhang, H.[Hao],
Neighborhood Region Smoothing Regularization for Finding Flat Minima in
Deep Neural Networks,
ACCV22(I:652-665).
Springer DOI
2307
BibRef
Serrano-e-Silva, P.[Pedro],
Cruz, R.[Ricardo],
Shihavuddin, A.S.M.,
Gonçalves, T.[Tiago],
Interpretability-guided Human Feedback During Neural Network Training,
IbPRIA23(276-287).
Springer DOI
2307
User guided training.
BibRef
Penzel, N.[Niklas],
Reimers, C.[Christian],
Bodesheim, P.[Paul],
Denzler, J.[Joachim],
Investigating Neural Network Training on a Feature Level Using
Conditional Independence,
CiV22(383-399).
Springer DOI
2304
BibRef
Bishay, M.[Mina],
Ghoneim, A.[Ahmed],
Ashraf, M.[Mohamed],
Mavadati, M.[Mohammad],
Which CNNs and Training Settings to Choose for Action Unit Detection?
A Study Based on a Large-Scale Dataset,
FG21(1-5)
IEEE DOI
2303
Training, Face recognition, Gesture recognition,
Computational efficiency, Convolutional neural networks
BibRef
Downes, J.[Justin],
Gleave, W.[Will],
Nakada, D.[Dan],
RarePlanes Soar Higher: Self-Supervised Pretraining for Resource
Constrained and Synthetic Datasets,
Pretrain23(1-9)
IEEE DOI
2302
Training, Conferences, Computational modeling, Data models,
Task analysis, Remote sensing
BibRef
Verma, V.K.[Vinay Kumar],
Mehta, N.[Nikhil],
Si, S.[Shijing],
Henao, R.[Ricardo],
Carin, L.[Lawrence],
Pushing the Efficiency Limit Using Structured Sparse Convolutions,
WACV23(6492-6502)
IEEE DOI
2302
Deep learning, Training, Convolutional codes, Tensors, Convolution,
Computational modeling, Neural networks, algorithms (including transfer)
BibRef
Nag, S.[Sayan],
Bhattacharyya, M.[Mayukh],
Mukherjee, A.[Anuraag],
Kundu, R.[Rohit],
Serf: Towards better training of deep neural networks using
log-Softplus ERror activation Function,
WACV23(5313-5322)
IEEE DOI
2302
Training, Deep learning, Neural networks, Object detection,
Machine translation, Vision + language and/or other modalities
BibRef
Dubey, S.R.[Shiv Ram],
Singh, S.K.[Satish Kumar],
Chaudhuri, B.B.[Bidyut Baran],
AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNs,
WACV23(5273-5282)
IEEE DOI
2302
Training, Stochastic processes, Benchmark testing, Boosting, History,
Convolutional neural networks, visual reasoning
BibRef
Ni, T.W.[Tian-Wei],
Ehsani, K.[Kiana],
Weihs, L.[Luca],
Salvador, J.[Jordi],
Towards Disturbance-Free Visual Mobile Manipulation,
WACV23(5208-5220)
IEEE DOI
2302
Training, Deep learning, Visualization, Costs, Navigation,
Reinforcement learning, Robotics
BibRef
Deng, W.J.[Wei-Jian],
Suh, Y.[Yumin],
Yu, X.[Xiang],
Faraki, M.[Masoud],
Zheng, L.[Liang],
Chandraker, M.[Manmohan],
Split to Learn: Gradient Split for Multi-Task Human Image Analysis,
WACV23(4340-4349)
IEEE DOI
2302
Training, Image analysis, Convolution, Computational modeling,
Estimation, Interference, Algorithms: Biometrics, face, gesture,
visual reasoning
BibRef
Watson, M.[Matthew],
Hasan, B.A.S.[Bashar Awwad Shiekh],
Moubayed, N.A.[Noura Al],
Learning How to MIMIC: Using Model Explanations to Guide Deep
Learning Training,
WACV23(1461-1470)
IEEE DOI
2302
Training, Deep learning, Measurement, Heating systems,
Analytical models, Visualization, MIMICs
BibRef
Popovic, N.[Nikola],
Chakraborty, R.[Ritika],
Dadon, D.[David],
Fried, O.[Ohad],
Hel-Or, Y.[Yacov],
DDNeRF: Depth Distribution Neural Radiance Fields,
WACV23(755-763)
IEEE DOI
2302
Training, Solid modeling, Computational modeling, Neural networks,
Predictive models, Algorithms: 3D computer vision,
image and video synthesis
BibRef
Fang, X.Y.[Xiao-Yu],
Wang, L.L.[Lin-Lin],
Liu, C.[Chang],
Hong, T.[Tao],
An Improved Method of Image Recognition with Deep Learning Combined
with Attention Mechanism,
ICIVC22(593-598)
IEEE DOI
2301
Training, Deep learning, Image recognition, Costs,
Computational modeling, Data models, attention mechanism
BibRef
Pan, J.[Jie],
Hu, H.G.[Hai-Gen],
Liu, A.[Aizhu],
Zhou, Q.W.[Qian-Wei],
Guan, Q.[Qiu],
A Channel-Spatial Hybrid Attention Mechanism using Channel Weight
Transfer Strategy,
ICPR22(2524-2531)
IEEE DOI
2212
WWW Link. Deep learning, Codes, Computational modeling, Reproducibility of results,
Complexity theory
BibRef
Xing, E.[Eric],
Liu, L.L.[Liang-Liang],
Xing, X.[Xin],
Qu, Y.[Yunni],
Jacobs, N.[Nathan],
Liang, G.[Gongbo],
Neural Network Decision-Making Criteria Consistency Analysis via
Inputs Sensitivity,
ICPR22(2328-2334)
IEEE DOI
2212
Training, Sensitivity, Uncertainty, Decision making, Closed box,
Artificial neural networks, Safety
BibRef
Zhu, Z.Z.[Ze-Zhou],
Dong, Y.[Yuan],
Non-uniform Piecewise Linear Activation Functions in Deep Neural
Networks,
ICPR22(2107-2113)
IEEE DOI
2212
Performance evaluation, Deep learning, Image edge detection,
Computational modeling, Neural networks, Distributed databases
BibRef
Dröge, H.[Hannah],
Möllenhoff, T.[Thomas],
Möller, M.[Michael],
Non-Smooth Energy Dissipating Networks,
ICIP22(3281-3285)
IEEE DOI
2211
Deep learning, Costs, Noise reduction, Neural networks,
Predictive models, Cost function, Iterative methods,
moreau envelope
BibRef
Barucic, D.[Denis],
Kybic, J.[Jan],
Fast Learning from Label Proportions with Small Bags,
ICIP22(3156-3160)
IEEE DOI
2211
Learn instances based on how many samples.
Training, Deep learning, Codes, Annotations, Neural networks,
Approximation algorithms, learning from label proportions, deep learning
BibRef
Berk, J.[Jane],
Jaszewski, M.[Martin],
Deledalle, C.A.[Charles-Alban],
Parameswaran, S.[Shibin],
U-Deepdig: Scalable Deep Decision Boundary Instance Generation,
ICIP22(2961-2965)
IEEE DOI
2211
Deep learning, Training, Knowledge engineering, Uncertainty,
Neural networks, Classification algorithms
BibRef
Qi, S.[Siyu],
Chamain, L.D.[Lahiru D.],
Ding, Z.[Zhi],
Hierarchical Training for Distributed Deep Learning Based on
Multimedia Data over Band-Limited Networks,
ICIP22(2871-2875)
IEEE DOI
2211
Training, Deep learning, Wireless communication,
Wireless sensor networks, Image coding, Robustness, Decoding,
information theory
BibRef
Zhen, X.J.[Xing-Jian],
Meng, Z.H.[Zi-Hang],
Chakraborty, R.[Rudrasis],
Singh, V.[Vikas],
On the Versatile Uses of Partial Distance Correlation in Deep Learning,
ECCV22(XXVI:327-346).
Springer DOI
2211
BibRef
Chrysos, G.G.[Grigorios G.],
Georgopoulos, M.[Markos],
Deng, J.K.[Jian-Kang],
Kossaifi, J.[Jean],
Panagakis, Y.[Yannis],
Anandkumar, A.[Anima],
Augmenting Deep Classifiers with Polynomial Neural Networks,
ECCV22(XXV:692-716).
Springer DOI
2211
BibRef
Chen, X.[Xuxi],
Chen, T.L.[Tian-Long],
Cheng, Y.[Yu],
Chen, W.Z.[Wei-Zhu],
Awadallah, A.[Ahmed],
Wang, Z.Y.[Zhang-Yang],
Scalable Learning to Optimize: A Learned Optimizer Can Train Big Models,
ECCV22(XXIII:389-405).
Springer DOI
2211
BibRef
Henig, A.[Amit],
Giryes, R.[Raja],
Utilizing Excess Resources in Training Neural Networks,
ICIP22(1941-1945)
IEEE DOI
2211
Training, Filtering, Computational modeling, Neural networks,
Supervised learning, Transfer learning, Semisupervised learning,
kernel filtering / composition
BibRef
Cheng, J.C.[Jia-Cheng],
Vasconcelos, N.M.[Nuno M.],
Calibrating Deep Neural Networks by Pairwise Constraints,
CVPR22(13699-13708)
IEEE DOI
2210
Deep learning, Training, Neural networks,
Calibration, Pattern recognition
BibRef
Zietlow, D.[Dominik],
Lohaus, M.[Michael],
Balakrishnan, G.[Guha],
Kleindessner, M.[Matthäus],
Locatello, F.[Francesco],
Schölkopf, B.[Bernhard],
Russell, C.[Chris],
Leveling Down in Computer Vision:
Pareto Inefficiencies in Fair Deep Classifiers,
CVPR22(10400-10411)
IEEE DOI
2210
Degradation, Ethics, Adaptation models, Computational modeling,
Buildings, Network architecture, Transparency, fairness,
Datasets and evaluation
BibRef
Cheng, F.[Feng],
Xu, M.Z.[Ming-Ze],
Xiong, Y.J.[Yuan-Jun],
Chen, H.[Hao],
Li, X.Y.[Xin-Yu],
Li, W.[Wei],
Xia, W.[Wei],
Stochastic Backpropagation:
A Memory Efficient Strategy for Training Video Models,
CVPR22(8291-8300)
IEEE DOI
2210
Training, Backpropagation, Computational modeling,
Memory management, Redundancy, Stochastic processes,
Video analysis and understanding
BibRef
Chavan, A.[Arnav],
Tiwari, R.[Rishabh],
Bamba, U.[Udbhav],
Gupta, D.K.[Deepak K.],
Dynamic Kernel Selection for Improved Generalization and Memory
Efficiency in Meta-learning,
CVPR22(9841-9850)
IEEE DOI
2210
Performance evaluation, Deep learning, Computational modeling,
Memory management, Network architecture, Pattern recognition,
Efficient learning and inferences
BibRef
Ronen, M.[Meitar],
Finder, S.E.[Shahaf E.],
Freifeld, O.[Oren],
DeepDPM: Deep Clustering With an Unknown Number of Clusters,
CVPR22(9851-9860)
IEEE DOI
2210
Deep learning, Training, Codes, Statistical analysis, Scalability,
Computational modeling, Statistical methods
BibRef
Biswas, K.[Koushik],
Kumar, S.[Sandeep],
Banerjee, S.[Shilpak],
Pandey, A.K.[Ashish Kumar],
Smooth Maximum Unit: Smooth Activation Function for Deep Networks
using Smoothing Maximum Technique,
CVPR22(784-793)
IEEE DOI
2210
Deep learning, Training, Image segmentation, Smoothing methods,
Semantics, Neural networks, Object detection, Optimization methods
BibRef
Huang, L.[Lei],
Zhou, Y.[Yi],
Wang, T.[Tian],
Luo, J.[Jie],
Liu, X.L.[Xiang-Long],
Delving into the Estimation Shift of Batch Normalization in a Network,
CVPR22(753-762)
IEEE DOI
2210
Deep learning, Training, Representation learning, Sociology,
Estimation,
Representation learning
BibRef
Liu, B.Y.[Bing-Yuan],
Ben Ayed, I.[Ismail],
Galdran, A.[Adrian],
Dolz, J.[Jose],
The Devil is in the Margin:
Margin-based Label Smoothing for Network Calibration,
CVPR22(80-88)
IEEE DOI
2210
WWW Link. Deal with over confident predictions.
Training, Image segmentation, Smoothing methods, Semantics,
Neural networks, Minimization, Calibration, Machine learning
BibRef
Loukili, S.E.[Salah Eddine],
Ezzati, A.[Abdellah],
Alla, S.B.[Said Ben],
Zraibi, B.[Brahim],
Reducing the training time of deep learning models using synchronous
SGD and large batch size,
ISCV22(1-3)
IEEE DOI
2208
Training, Deep learning, Computational modeling, Neural networks,
Stochastic processes, Parallel processing, Throughput,
convolutional neural network
BibRef
Yuan, K.[Kun],
Chen, Y.M.[Yi-Ming],
Huang, X.M.[Xin-Meng],
Zhang, Y.Y.[Ying-Ya],
Pan, P.[Pan],
Xu, Y.H.[Ying-Hui],
Yin, W.T.[Wo-Tao],
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training,
ICCV21(3009-3019)
IEEE DOI
2203
Training, Degradation, Deep learning, Runtime,
Computational modeling, Task analysis,
Machine learning architectures and formulations
BibRef
Ramé, A.[Alexandre],
Sun, R.[Rémy],
Cord, M.[Matthieu],
MixMo:
Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks,
ICCV21(803-813)
IEEE DOI
2203
Training, Fitting, Data models, Image classification,
Recognition and classification,
Optimization and learning methods
BibRef
Tiwari, L.[Lokender],
Madan, A.[Anish],
Anand, S.[Saket],
Banerjee, S.[Subhashis],
REGroup: Rank-aggregating Ensemble of Generative Classifiers for
Robust Predictions,
WACV22(3829-3838)
IEEE DOI
2202
Training, Deep learning, Perturbation methods, Neurons,
Neural networks, Training data, Predictive models,
Vision Systems and Applications Deep Learning
BibRef
Boyd, A.[Aidan],
Tinsley, P.[Patrick],
Bowyer, K.W.[Kevin W.],
Czajka, A.[Adam],
CYBORG: Blending Human Saliency Into the Loss Improves Deep
Learning-Based Synthetic Face Detection,
WACV23(6097-6106)
IEEE DOI
2302
Training, Visualization, Training data, Brain modeling, Data models,
Face detection, Task analysis, Algorithms: Biometrics, face, gesture,
Psychology and cognitive science
BibRef
Boyd, A.[Aidan],
Bowyer, K.W.[Kevin W.],
Czajka, A.[Adam],
Human-Aided Saliency Maps Improve Generalization of Deep Learning,
WACV22(1255-1264)
IEEE DOI
2202
Deep learning, Training, Image coding,
Biometrics (access control), Human intelligence, Security/Surveillance
BibRef
Fields, G.[Greg],
Samragh, M.[Mohammad],
Javaheripi, M.[Mojan],
Koushanfar, F.[Farinaz],
Javidi, T.[Tara],
Trojan Signatures in DNN Weights,
AROW21(12-20)
IEEE DOI
2112
Training, Deep learning,
Computational modeling, Trojan horses
BibRef
Lebrat, L.[Léo],
Cruz, R.S.[Rodrigo Santa],
Fookes, C.[Clinton],
Salvado, O.[Olivier],
MongeNet: Efficient Sampler for Geometric Deep Learning,
CVPR21(16659-16668)
IEEE DOI
2111
Training, Deep learning, Solid modeling,
Computational modeling, Estimation, Approximation error
BibRef
Durasov, N.[Nikita],
Bagautdinov, T.[Timur],
Baque, P.[Pierre],
Fua, P.[Pascal],
Masksembles for Uncertainty Estimation,
CVPR21(13534-13543)
IEEE DOI
2111
Deep learning, Training, Uncertainty, Costs, Computational modeling,
Estimation, Reinforcement learning
BibRef
Lin, Y.[Yi],
Wang, N.[Namin],
Ma, X.Q.[Xiao-Qing],
Li, Z.W.[Zi-Wei],
Bai, G.[Gang],
How Does DCNN Make Decisions ?,
ICPR21(3342-3349)
IEEE DOI
2105
Training, Convolution, Semantics, Decision making,
Visual systems, Feature extraction,
Deep Convolutional Neural Network
BibRef
Mastan, I.D.[Indra Deep],
Raman, S.[Shanmuganathan],
DeepCFL: Deep Contextual Features Learning from a Single Image,
WACV21(2896-2905)
IEEE DOI
2106
Training, Image synthesis, Semantics,
Training data, Image restoration
BibRef
Kobayashi, T.[Takumi],
Phase-wise Parameter Aggregation For Improving SGD Optimization,
WACV21(2624-2633)
IEEE DOI
2106
Stochastic gradient descent. Applied to deep net training.
Training, Optimization methods,
Convolutional neural networks, Task analysis, Surface treatment
BibRef
Ding, P.L.K.[Pak Lun Kevin],
Martin, S.[Sarah],
Li, B.X.[Bao-Xin],
Improving Batch Normalization with Skewness Reduction for Deep Neural
Networks,
ICPR21(7165-7172)
IEEE DOI
2105
Training, Neural networks, Transforms, Optimization
BibRef
Liao, W.H.[Wen-Hung],
Huang, Y.T.[Yen-Ting],
Investigation of DNN Model Robustness Using Heterogeneous Datasets,
ICPR21(4393-4397)
IEEE DOI
2105
Training, Deep learning, Image coding, Neural networks,
Training data, Feature extraction, Data models
BibRef
Nakada, M.[Masaki],
Chen, H.[Honglin],
Lakshmipathy, A.[Arjun],
Terzopoulos, D.[Demetri],
Locally-Connected, Irregular Deep Neural Networks for Biomimetic
Active Vision in a Simulated Human,
ICPR21(4465-4472)
IEEE DOI
2105
Visualization, Solid modeling,
Biological system modeling, Training data, Prototypes, Retina
BibRef
Berg, A.[Axel],
Oskarsson, M.[Magnus],
O'Connor, M.[Mark],
Deep Ordinal Regression with Label Diversity,
ICPR21(2740-2747)
IEEE DOI
2105
Training, Learning systems, Neural networks, Pose estimation,
Diversity methods, Predictive models, Search problems
BibRef
Hansen, P.[Patrick],
Vilkin, A.[Alexey],
Krustalev, Y.[Yury],
Imber, J.[James],
Talagala, D.[Dumidu],
Hanwell, D.[David],
Mattina, M.[Matthew],
Whatmough, P.N.[Paul N.],
ISP4ML: The Role of Image Signal Processing in Efficient Deep
Learning Vision Systems,
ICPR21(2438-2445)
IEEE DOI
2105
Training, Machine vision, Pipelines, Memory management,
Signal processing, Market research, Software
BibRef
Brigato, L.[Lorenzo],
Barz, B.[Björn],
Iocchi, L.[Luca],
Denzler, J.[Joachim],
Tune It or Don't Use It:
Benchmarking Data-Efficient Image Classification,
VIPriors21(1071-1080)
IEEE DOI
2112
Deep learning, Training, Satellites, Head, Pipelines,
Benchmark testing, Standards
BibRef
Brigato, L.[Lorenzo],
Iocchi, L.[Luca],
A Close Look at Deep Learning with Small Data,
ICPR21(2490-2497)
IEEE DOI
2105
Training, Deep learning, Computational modeling, Pipelines,
Benchmark testing, Complexity theory
BibRef
Du, S.Y.[Shuai-Yuan],
Hong, C.Y.[Chao-Yi],
Pan, Z.Y.[Zhi-Yu],
Feng, C.[Chen],
Cao, Z.G.[Zhi-Guo],
Parallel Network to Learn Novelty from the Known,
ICPR21(2172-2179)
IEEE DOI
2105
Training, Aggregates, Training data, Benchmark testing, Data models,
Anomaly detection
BibRef
Meng, L.H.[Ling-Heng],
Gorbet, R.[Rob],
Kulic, D.[Dana],
The Effect of Multi-step Methods on Overestimation in Deep
Reinforcement Learning,
ICPR21(347-353)
IEEE DOI
2105
Computational modeling, Neural networks, Buildings,
Reinforcement learning, Approximation error
BibRef
Georgiou, T.[Theodoros],
Schmitt, S.[Sebastian],
Bäck, T.[Thomas],
Chen, W.[Wei],
Lew, M.[Michael],
Norm Loss: An efficient yet effective regularization method for deep
neural networks,
ICPR21(8812-8818)
IEEE DOI
2105
Training, Manifolds, Neural networks,
Benchmark testing, Pattern recognition, Computational efficiency
BibRef
Deng, X.[Xiang],
Zhang, Z.F.M.[Zhong-Fei Mark],
Is the Meta-Learning Idea Able to Improve the Generalization of Deep
Neural Networks on the Standard Supervised Learning?,
ICPR21(150-157)
IEEE DOI
2105
Training, Supervised learning, Neural networks, Benchmark testing,
Linear programming, Pattern recognition, Computational efficiency
BibRef
Beeching, E.[Edward],
Debangoye, J.[Jilles],
Simonin, O.[Oliver],
Wolf, C.[Christian],
Deep Reinforcement Learning on a Budget:
3D Control and Reasoning Without a Supercomputer,
ICPR21(158-165)
IEEE DOI
2105
Training, Solid modeling,
Reinforcement learning, Benchmark testing, Cognition, Supercomputers
BibRef
Li, X.L.[Xi-Lai],
Sun, W.[Wei],
Wu, T.F.[Tian-Fu],
Attentive Normalization,
ECCV20(XVII:70-87).
Springer DOI
2011
BibRef
Ma, X.,
Qiao, Z.,
Guo, J.,
Tang, S.,
Chen, Q.,
Yang, Q.,
Fu, S.,
Cascaded Context Dependency: An Extremely Lightweight Module For Deep
Convolutional Neural Networks,
ICIP20(1741-1745)
IEEE DOI
2011
Feature extraction, Task analysis,
Object detection, Charge coupled devices, Training,
multi-scale
BibRef
Oyedotun, O.K.[Oyebade K.],
Shabayek, A.E.[Abd El_Rahman],
Aouada, D.[Djamila],
Ottersten, B.[Björn],
Revisiting the Training of Very Deep Neural Networks without Skip
Connections,
ICPR21(2724-2731)
IEEE DOI
2105
BibRef
Earlier:
Going Deeper With Neural Networks Without Skip Connections,
ICIP20(1756-1760)
IEEE DOI
2011
Training, Neural networks, Explosions, Pattern recognition,
Task analysis, Error analysis, Optimization,
Computational modeling, Deep neural network, PlainNet, classification
BibRef
Rao, K.[Kanishka],
Harris, C.[Chris],
Irpan, A.[Alex],
Levine, S.[Sergey],
Ibarz, J.[Julian],
Khansari, M.[Mohi],
RL-CycleGAN: Reinforcement Learning Aware Simulation-to-Real,
CVPR20(11154-11163)
IEEE DOI
2008
Task analysis, Robots, Adaptation models, Grasping,
Training, Learning (artificial intelligence)
BibRef
Shi, Y.[Yi],
Xu, M.C.[Meng-Chen],
Yuan, S.H.[Shuai-Hang],
Fang, Y.[Yi],
Unsupervised Deep Shape Descriptor With Point Distribution Learning,
CVPR20(9350-9359)
IEEE DOI
2008
To learn shapes.
Shape, Robustness, Decoding, Solid modeling, Training, Neural networks
BibRef
Xu, Y.H.[Yuan-Hong],
Qian, Q.[Qi],
Li, H.[Hao],
Jin, R.[Rong],
Hu, J.[Juhua],
Weakly Supervised Representation Learning with Coarse Labels,
ICCV21(10573-10581)
IEEE DOI
2203
Deep learning, Training, Representation learning, Visualization,
Benchmark testing, Data collection, Representation learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Huang, L.[Lei],
Liu, L.[Li],
Zhu, F.[Fan],
Wan, D.W.[Di-Wen],
Yuan, Z.H.[Ze-Huan],
Li, B.[Bo],
Shao, L.[Ling],
Controllable Orthogonalization in Training DNNs,
CVPR20(6428-6437)
IEEE DOI
2008
Training, Matrix decomposition, Convergence, Neural networks,
Eigenvalues and eigenfunctions, Covariance matrices, Jacobian matrices
BibRef
Zhuang, C.,
She, T.,
Andonian, A.,
Sobol Mark, M.,
Yamins, D.,
Unsupervised Learning From Video With Deep Neural Embeddings,
CVPR20(9560-9569)
IEEE DOI
2008
Task analysis, Visualization, Unsupervised learning,
Neural networks, Image recognition
BibRef
Xie, X.,
Kim, K.,
Partial Weight Adaptation for Robust DNN Inference,
CVPR20(9570-9578)
IEEE DOI
2008
Distortion, Training, Streaming media, Robustness,
Frequency response, Sensitivity, Training data
BibRef
Li, P.,
Zhao, H.,
Liu, H.,
Deep Fair Clustering for Visual Learning,
CVPR20(9067-9076)
IEEE DOI
2008
Clustering algorithms, Visualization, Partitioning algorithms,
Clustering methods, Machine learning, Training, Measurement
BibRef
Mittal, G.,
Liu, C.,
Karianakis, N.,
Fragoso, V.,
Chen, M.,
Fu, Y.,
HyperSTAR: Task-Aware Hyperparameters for Deep Networks,
CVPR20(8733-8742)
IEEE DOI
2008
Task analysis, Acceleration, Training, Visualization, Optimization,
Bayes methods, Gaussian processes
BibRef
Huang, J.,
Gong, S.,
Zhu, X.,
Deep Semantic Clustering by Partition Confidence Maximisation,
CVPR20(8846-8855)
IEEE DOI
2008
Indexes, Training, Uncertainty, Semantics, Visualization,
Clustering methods, Machine learning
BibRef
Joneidi, M.,
Vahidian, S.,
Esmaeili, A.,
Wang, W.,
Rahnavard, N.,
Lin, B.,
Shah, M.,
Select to Better Learn: Fast and Accurate Deep Learning Using Data
Selection From Nonlinear Manifolds,
CVPR20(7816-7826)
IEEE DOI
2008
Manifolds, Training, Machine learning, Face, Time complexity
BibRef
Chrysos, G.G.[Grigorios G.],
Moschoglou, S.[Stylianos],
Bouritsas, G.[Giorgos],
Panagakis, Y.[Yannis],
Deng, J.K.[Jian-Kang],
Zafeiriou, S.P.[Stefanos P.],
P-nets: Deep Polynomial Neural Networks,
CVPR20(7323-7333)
IEEE DOI
2008
Neural networks, Tensile stress, Task analysis, Training,
Image generation
BibRef
Meng, F.,
Cheng, H.,
Li, K.,
Xu, Z.,
Ji, R.,
Sun, X.,
Lu, G.,
Filter Grafting for Deep Neural Networks,
CVPR20(6598-6606)
IEEE DOI
2008
Training, Information filters, Filtering algorithms,
Filtering theory, Entropy, Nickel
BibRef
Zhan, X.,
Xie, J.,
Liu, Z.,
Ong, Y.,
Loy, C.C.,
Online Deep Clustering for Unsupervised Representation Learning,
CVPR20(6687-6696)
IEEE DOI
2008
Training, Feature extraction, Task analysis, Frequency modulation,
Learning systems, Visualization, Clustering algorithms
BibRef
Liu, J.,
Sun, Y.,
Han, C.,
Dou, Z.,
Li, W.,
Deep Representation Learning on Long-Tailed Data:
A Learnable Embedding Augmentation Perspective,
CVPR20(2967-2976)
IEEE DOI
2008
Training, Task analysis, Head, Distortion, Face recognition,
Visualization, Additives
BibRef
Chen, H.,
Wang, Y.,
Xu, C.,
Shi, B.,
Xu, C.,
Tian, Q.,
Xu, C.,
AdderNet: Do We Really Need Multiplications in Deep Learning?,
CVPR20(1465-1474)
IEEE DOI
2008
Convolutional codes, Adders, Measurement, Computational complexity,
Biological neural networks, Training
BibRef
Xu, A.,
Huo, Z.,
Huang, H.,
On the Acceleration of Deep Learning Model Parallelism With Staleness,
CVPR20(2085-2094)
IEEE DOI
2008
Training, Parallel processing, Computational modeling,
Neural networks, Stochastic processes, Acceleration, Convergence
BibRef
Singh, P.,
Varshney, M.,
Namboodiri, V.P.,
Cooperative Initialization based Deep Neural Network Training,
WACV20(1130-1139)
IEEE DOI
2006
Training, Neurons, Standards, Task analysis, Color, Neural networks,
Computer science
BibRef
Lo, E.,
Kohl, J.,
Internet of Things (IoT) Discovery Using Deep Neural Networks,
WACV20(795-803)
IEEE DOI
2006
Object detection, Training,
Internet of Things, Modulation, Spectrogram, Microprocessors
BibRef
Li, H.,
Zhang, H.,
Qi, X.,
Ruigang, Y.,
Huang, G.,
Improved Techniques for Training Adaptive Deep Networks,
ICCV19(1891-1900)
IEEE DOI
2004
gradient methods, graph theory, groupware, image classification,
inference mechanisms, learning (artificial intelligence), Knowledge transfer
BibRef
Haeusser, P.[Philip],
Plapp, J.[Johannes],
Golkov, V.[Vladimir],
Aljalbout, E.[Elie],
Cremers, D.[Daniel],
Associative Deep Clustering:
Training a Classification Network with No Labels,
GCPR18(18-32).
Springer DOI
1905
BibRef
Keller, M.[Michel],
Chen, Z.[Zetao],
Maffra, F.[Fabiola],
Schmuck, P.[Patrik],
Chli, M.[Margarita],
Learning Deep Descriptors with Scale-Aware Triplet Networks,
CVPR18(2762-2770)
IEEE DOI
1812
Training, Neural networks, Task analysis, Feature extraction
BibRef
Goh, C.K.,
Liu, Y.,
Kong, A.W.K.,
A Constrained Deep Neural Network for Ordinal Regression,
CVPR18(831-839)
IEEE DOI
1812
Optimization, Feature extraction, Neural networks,
Support vector machines, Training, Measurement
BibRef
Alqahtani, A.,
Xie, X.,
Deng, J.,
Jones, M.W.,
Learning Discriminatory Deep Clustering Models,
CAIP19(I:224-233).
Springer DOI
1909
BibRef
And:
A Deep Convolutional Auto-Encoder with Embedded Clustering,
ICIP18(4058-4062)
IEEE DOI
1809
Image reconstruction, Training, Feature extraction,
Linear programming, Task analysis, Convolution, Cost function,
Embedded Clustering
BibRef
Chun, Y.,
Fessler, J.A.,
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for
Iterative Image Recovery,
IVMSP18(1-5)
IEEE DOI
1809
Training, Convolution, Iterative decoding, Thresholding (Imaging),
Imaging, Decoding, Encoding
BibRef
Huang, L.,
Liu, X.,
Liu, Y.,
Lang, B.,
Tao, D.,
Centered Weight Normalization in Accelerating Training of Deep Neural
Networks,
ICCV17(2822-2830)
IEEE DOI
1802
learning (artificial intelligence), multilayer perceptrons,
neural nets, centered weight normalization,
Training
BibRef
Qiu, Z.,
Yao, T.,
Mei, T.,
Deep Quantization: Encoding Convolutional Activations with Deep
Generative Model,
CVPR17(4085-4094)
IEEE DOI
1711
Computational modeling, Encoding,
Optimization, Quantization (signal), Training, Visualization
BibRef
Diba, A.[Ali],
Sharma, V.[Vivek],
Pazandeh, A.,
Pirsiavash, H.,
Van Gool, L.J.[Luc J.],
Weakly Supervised Cascaded Convolutional Networks,
CVPR17(5131-5139)
IEEE DOI
1711
Feature extraction, Object detection,
Proposals, Reliability, Training
BibRef
Cruz, R.S.,
Fernando, B.[Basura],
Cherian, A.,
Gould, S.[Stephen],
DeepPermNet: Visual Permutation Learning,
CVPR17(6044-6052)
IEEE DOI
1711
Computational modeling, Image sequences,
Machine learning, Training, Visualization
BibRef
Zheng, S.,
Song, Y.,
Leung, T.,
Goodfellow, I.J.[Ian J.],
Improving the Robustness of Deep Neural Networks via Stability
Training,
CVPR16(4480-4488)
IEEE DOI
1612
BibRef
Iandola, F.N.,
Moskewicz, M.W.,
Ashraf, K.,
Keutzer, K.,
FireCaffe: Near-Linear Acceleration of Deep Neural Network Training
on Compute Clusters,
CVPR16(2592-2600)
IEEE DOI
1612
BibRef
Blot, M.,
Robert, T.,
Thome, N.,
Cord, M.,
Shade: Information-Based Regularization for Deep Learning,
ICIP18(813-817)
IEEE DOI
1809
Entropy, Training, Task analysis, Machine learning, Neurons, Standards,
Optimization, Deep learning, regularization, invariance,
image understanding
BibRef
Liu, M.Y.[Ming-Yu],
Mallya, A.[Arun],
Tuzel, O.[Oncel],
Chen, X.[Xi],
Unsupervised network pretraining via encoding human design,
WACV16(1-9)
IEEE DOI
1606
Computer architecture. Deep NN training.
BibRef
Wang, M.[Min],
Liu, B.Y.[Bao-Yuan],
Foroosh, H.[Hassan],
Look-Up Table Unit Activation Function for Deep Convolutional Neural
Networks,
WACV18(1225-1233)
IEEE DOI
1806
BibRef
Earlier:
Factorized Convolutional Neural Networks,
Matrix-Tensor17(545-553)
IEEE DOI
1802
Gaussian processes, convolution, data structures,
feedforward neural nets, image recognition, interpolation,
Training.
Complexity theory, Convolution, Kernel, Standards,
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Deep Learning with Noisy Labels, Robust Deep Learning .