14.5.8.6.6 Forgetting, Explaination, Intrepretation, Understanding of Convolutional Neural Networks

Chapter Contents (Back)
Convolutional Neural Networks. Forgetting. Explainable. CNN.

Chung, F.L., Wang, S., Deng, Z., Hu, D.,
CATSMLP: Toward a Robust and Interpretable Multilayer Perceptron With Sigmoid Activation Functions,
SMC-B(36), No. 6, December 2006, pp. 1319-1331.
IEEE DOI 0701
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Peharz, R.[Robert], Gens, R.[Robert], Pernkopf, F.[Franz], Domingos, P.[Pedro],
On the Latent Variable Interpretation in Sum-Product Networks,
PAMI(39), No. 10, October 2017, pp. 2030-2044.
IEEE DOI 1709
Bayes methods, Computational modeling, Inference algorithms, Mixture models, Periodic structures, Probabilistic logic, Semantics, MPE inference, Sum-product networks, expectation-maximization, latent variables, mixture, models See also Sum-product networks: A new deep architecture. BibRef

Montavon, G.[Grégoire], Lapuschkin, S.[Sebastian], Binder, A.[Alexander], Samek, W.[Wojciech], Müller, K.R.[Klaus-Robert],
Explaining nonlinear classification decisions with deep Taylor decomposition,
PR(65), No. 1, 2017, pp. 211-222.
Elsevier DOI 1702
Deep neural networks BibRef

Lapuschkin, S., Binder, A., Montavon, G.[Grégoire], Müller, K.R.[Klaus-Robert], Samek, W.[Wojciech],
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks,
CVPR16(2912-2920)
IEEE DOI 1612
BibRef

Samangouei, P.[Pouya], Saeedi, A.[Ardavan], Nakagawa, L.[Liam], Silberman, N.[Nathan],
ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations,
ECCV18(X: 681-696).
Springer DOI 1810
BibRef

Li, Z.Z.[Zhi-Zhong], Hoiem, D.[Derek],
Learning Without Forgetting,
PAMI(40), No. 12, December 2018, pp. 2935-2947.
IEEE DOI 1811
BibRef
Earlier: ECCV16(IV: 614-629).
Springer DOI 1611
Keep the old results in NN, but learn new capability. Feature extraction, Deep learning, Training data, Neural networks, Convolutional neural networks, Knowledge engineering, visual recognition. BibRef

Li, Z.Z.[Zhi-Zhong], Hoiem, D.[Derek],
Improving Confidence Estimates for Unfamiliar Examples,
CVPR20(2683-2692)
IEEE DOI 2008
Training, Calibration, Dogs, Uncertainty, Cats, Task analysis, Testing BibRef

Mopuri, K.R., Garg, U., Babu, R.V.[R. Venkatesh],
CNN Fixations: An Unraveling Approach to Visualize the Discriminative Image Regions,
IP(28), No. 5, May 2019, pp. 2116-2125.
IEEE DOI 1903
computer vision, convolutional neural nets, feature extraction, object recognition, CNN fixations, discriminative image regions, weakly supervised localization BibRef

Kuo, C.C.J.[C.C. Jay], Zhang, M.[Min], Li, S.[Siyang], Duan, J.[Jiali], Chen, Y.[Yueru],
Interpretable convolutional neural networks via feedforward design,
JVCIR(60), 2019, pp. 346-359.
Elsevier DOI 1903
Interpretable machine learning, Convolutional neural networks, Principal component analysis, Dimension reduction BibRef

Chen, Y., Yang, Y., Wang, W., Kuo, C.C.J.,
Ensembles of Feedforward-Designed Convolutional Neural Networks,
ICIP19(3796-3800)
IEEE DOI 1910
Ensemble, Image classification, Interpretable CNN, Dimension reduction BibRef

Chen, Y., Yang, Y., Zhang, M., Kuo, C.C.J.,
Semi-Supervised Learning Via Feedforward-Designed Convolutional Neural Networks,
ICIP19(365-369)
IEEE DOI 1910
Semi-supervised learning, Ensemble, Image classification, Interpretable CNN BibRef

Li, H.[Heyi], Tian, Y.[Yunke], Mueller, K.[Klaus], Chen, X.[Xin],
Beyond saliency: Understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation,
IVC(83-84), 2019, pp. 70-86.
Elsevier DOI 1904
Convolutional neural networks, Deep learning understanding, Salient relevance map, Attention area BibRef

Fan, C.X.[Chun-Xiao], Li, Y.[Yang], Tian, L.[Lei], Li, Y.[Yong],
Rectifying Transformation Networks for Transformation-Invariant Representations with Power Law,
IEICE(E102-D), No. 3, March 2019, pp. 675-679.
WWW Link. 1904
CNN to rectify learned feature representations. BibRef

Cao, C.S.[Chun-Shui], Huang, Y.Z.[Yong-Zhen], Yang, Y.[Yi], Wang, L.[Liang], Wang, Z.L.[Zi-Lei], Tan, T.N.[Tie-Niu],
Feedback Convolutional Neural Network for Visual Localization and Segmentation,
PAMI(41), No. 7, July 2019, pp. 1627-1640.
IEEE DOI 1906
Neurons, Visualization, Image segmentation, Semantics, Convolutional neural networks, Task analysis, object segmentation BibRef

Zhou, B.[Bolei], Bau, D.[David], Oliva, A.[Aude], Torralba, A.[Antonio],
Interpreting Deep Visual Representations via Network Dissection,
PAMI(41), No. 9, Sep. 2019, pp. 2131-2145.
IEEE DOI 1908
Method quantifies the interpretability of CNN representations. Visualization, Detectors, Training, Image color analysis, Task analysis, Image segmentation, Semantics, interpretable machine learning BibRef

Liu, R.S.[Ri-Sheng], Cheng, S.C.[Shi-Chao], Ma, L.[Long], Fan, X.[Xin], Luo, Z.X.[Zhong-Xuan],
Deep Proximal Unrolling: Algorithmic Framework, Convergence Analysis and Applications,
IP(28), No. 10, October 2019, pp. 5013-5026.
IEEE DOI 1909
Task analysis, Optimization, Convergence, Mathematical model, Network architecture, Computer architecture, Data models, low-level computer vision BibRef

Cui, X.R.[Xin-Rui], Wang, D.[Dan], Wang, Z.J.[Z. Jane],
Multi-Scale Interpretation Model for Convolutional Neural Networks: Building Trust Based on Hierarchical Interpretation,
MultMed(21), No. 9, September 2019, pp. 2263-2276.
IEEE DOI 1909
Visualization, Computational modeling, Analytical models, Feature extraction, Perturbation methods, Image segmentation, model-agnostic BibRef

Wang, W.[Wei], Zhu, L.[Liqiang], Guo, B.[Baoqing],
Reliable identification of redundant kernels for convolutional neural network compression,
JVCIR(63), 2019, pp. 102582.
Elsevier DOI 1909
Network compression, Convolutional neural network, Pruning criterion, Channel-level pruning BibRef

Hu, S.X.[Shell Xu], Zagoruyko, S.[Sergey], Komodakis, N.[Nikos],
Exploring weight symmetry in deep neural networks,
CVIU(187), 2019, pp. 102786.
Elsevier DOI 1909
BibRef

Selvaraju, R.R.[Ramprasaath R.], Cogswell, M.[Michael], Das, A.[Abhishek], Vedantam, R.[Ramakrishna], Parikh, D.[Devi], Batra, D.[Dhruv],
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,
IJCV(128), No. 2, February 2020, pp. 336-359.
Springer DOI 2002
BibRef
Earlier: ICCV17(618-626)
IEEE DOI 1802
Explain the CNN models. convolution, data visualisation, gradient methods, image classification, image representation, inference mechanisms, Visualization BibRef

Aich, S.[Shubhra], Yamazaki, M.[Masaki], Taniguchi, Y.[Yasuhiro], Stavness, I.[Ian],
Multi-Scale Weight Sharing Network for Image Recognition,
PRL(131), 2020, pp. 348-354.
Elsevier DOI 2004
Multi-scale weight sharing, Image recognition, Convolutional neural networks, Image classification BibRef

Saraee, E.[Elham], Jalal, M.[Mona], Betke, M.[Margrit],
Visual complexity analysis using deep intermediate-layer features,
CVIU(195), 2020, pp. 102949.
Elsevier DOI 2005
Visual complexity, Convolutional layers, Deep neural network, Feature extraction, Convolutional neural network, Scene classification BibRef

Xie, L., Lee, F., Liu, L., Yin, Z., Chen, Q.,
Hierarchical Coding of Convolutional Features for Scene Recognition,
MultMed(22), No. 5, May 2020, pp. 1182-1192.
IEEE DOI 2005
Visualization, Convolutional codes, Encoding, Image representation, Feature extraction, Image recognition, Image coding, Scene recognition BibRef

Gong, M.[Maoguo], Yao, C.Y.[Chuan-Yu], Xie, Y.[Yu], Xu, M.L.[Ming-Liang],
Semi-supervised network embedding with text information,
PR(104), 2020, pp. 107347.
Elsevier DOI 2005
Network embedding, Structure preserving, Text representation, Stacked auto-encoders BibRef

Rio-Torto, I.[Isabel], Fernandes, K.[Kelwin], Teixeira, L.F.[Luís F.],
Understanding the decisions of CNNs: An in-model approach,
PRL(133), 2020, pp. 373-380.
Elsevier DOI 2005
Explainable AI, Explainability, Interpretability, Deep Llearning, Convolutional Nneural Nnetworks BibRef

Shi, Y.C.[Yu-Cheng], Han, Y.[Yahong], Zhang, Q.[Quanxin], Kuang, X.H.[Xiao-Hui],
Adaptive iterative attack towards explainable adversarial robustness,
PR(105), 2020, pp. 107309.
Elsevier DOI 2006
Adversarial example, Adversarial attack, Image classification BibRef

Gao, X.J.[Xin-Jian], Zhang, Z.[Zhao], Mu, T.T.[Ting-Ting], Zhang, X.D.[Xu-Dong], Cui, C.[Chaoran], Wang, M.[Meng],
Self-attention driven adversarial similarity learning network,
PR(105), 2020, pp. 107331.
Elsevier DOI 2006
Self-attention mechanism, Adversarial loss, Similarity learning network, Explainable deep learning BibRef

Jung, A., Nardelli, P.H.J.,
An Information-Theoretic Approach to Personalized Explainable Machine Learning,
SPLetters(27), 2020, pp. 825-829.
IEEE DOI 2006
Predictive models, Data models, Probabilistic logic, Machine learning, Decision making, Linear regression, decision support systems BibRef

Wang, Y., Su, H., Zhang, B., Hu, X.,
Interpret Neural Networks by Extracting Critical Subnetworks,
IP(29), 2020, pp. 6707-6720.
IEEE DOI 2007
Predictive models, Logic gates, Neural networks, Machine learning, Feature extraction, Robustness, Visualization, adversarial robustness BibRef

Rickmann, A.M.[Anne-Marie], Roy, A.G.[Abhijit Guha], Sarasua, I.[Ignacio], Wachinger, C.[Christian],
Recalibrating 3D ConvNets With Project Excite,
MedImg(39), No. 7, July 2020, pp. 2461-2471.
IEEE DOI 2007
Biomedical imaging, Image segmentation, Task analysis, volumetric segmentation BibRef

Wang, Y., Su, H., Zhang, B., Hu, X.,
Learning Reliable Visual Saliency For Model Explanations,
MultMed(22), No. 7, July 2020, pp. 1796-1807.
IEEE DOI 2007
Visualization, Reliability, Predictive models, Task analysis, Perturbation methods, Backpropagation, Real-time systems, deep learning BibRef

Cui, X.R.[Xin-Rui], Wang, D.[Dan], Wang, Z.J.[Z. Jane],
Feature-Flow Interpretation of Deep Convolutional Neural Networks,
MultMed(22), No. 7, July 2020, pp. 1847-1861.
IEEE DOI 2007
Visualization, Computational modeling, Perturbation methods, Convolutional neural networks, Medical services, Birds, sparse representation BibRef

Rafegas, I.[Ivet], Vanrell, M.[Maria], Alexandre, L.A.[Luís A.], Arias, G.[Guillem],
Understanding trained CNNs by indexing neuron selectivity,
PRL(136), 2020, pp. 318-325.
Elsevier DOI 2008
Convolutional neural networks, Visualization of CNNs, Neuron selectivity, CNNs Understanding, Feature visualization, BibRef


Ding, Y.K.[Yu-Kun], Liu, J.L.[Jing-Lan], Xiong, J.J.[Jin-Jun], Shi, Y.Y.[Yi-Yu],
Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off,
TCV20(22-31)
IEEE DOI 2008
Uncertainty, Calibration, Estimation, Predictive models, Complexity theory, Neural networks BibRef

Ye, J.W.[Jing-Wen], Ji, Y.[Yixin], Wang, X.[Xinchao], Gao, X.[Xin], Song, M.L.[Ming-Li],
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN,
CVPR20(12513-12522)
IEEE DOI 2008
Multiple CNN. Generators, Training, Task analysis, Knowledge engineering, Training data BibRef

Yang, Z.X.[Zong-Xin], Zhu, L.C.[Lin-Chao], Wu, Y.[Yu], Yang, Y.[Yi],
Gated Channel Transformation for Visual Recognition,
CVPR20(11791-11800)
IEEE DOI 2008
Logic gates, Task analysis, Visualization, Neurons, Computer architecture, Training, Complexity theory BibRef

Guo, Q.S.[Qiu-Shan], Wang, X.J.[Xin-Jiang], Wu, Y.C.[Yi-Chao], Yu, Z.P.[Zhi-Peng], Liang, D.[Ding], Hu, X.L.[Xiao-Lin], Luo, P.[Ping],
Online Knowledge Distillation via Collaborative Learning,
CVPR20(11017-11026)
IEEE DOI 2008
Knowledge engineering, Training, Collaborative work, Perturbation methods, Collaboration, Neural networks, Logic gates BibRef

Li, T., Li, J., Liu, Z., Zhang, C.,
Few Sample Knowledge Distillation for Efficient Network Compression,
CVPR20(14627-14635)
IEEE DOI 2008
Training, Tensile stress, Knowledge engineering, Convolution, Neural networks, Computational modeling, Standards BibRef

Xu, S.[Shawn], Venugopalan, S.[Subhashini], Sundararajan, M.[Mukund],
Attribution in Scale and Space,
CVPR20(9677-9686)
IEEE DOI 2008
Code, Deep Nets.
WWW Link. Perturbation methods, Task analysis, Kernel, Mathematical model, Google, Medical services BibRef

Wang, Z., Mardziel, P., Datta, A., Fredrikson, M.,
Interpreting Interpretations: Organizing Attribution Methods by Criteria,
TCV20(48-55)
IEEE DOI 2008
Perturbation methods, Visualization, Computational modeling, Measurement, Convolutional neural networks, Dogs BibRef

Taylor, E., Shekhar, S., Taylor, G.W.,
Response Time Analysis for Explainability of Visual Processing in CNNs,
MVM20(1555-1558)
IEEE DOI 2008
Grammar, Computational modeling, Semantics, Syntactics, Visualization, Analytical models, Object recognition BibRef

Choi, H., Som, A., Turaga, P.,
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification,
Diff-CVML20(3659-3666)
IEEE DOI 2008
Training, Task analysis, Feature extraction, Euclidean distance, Airplanes, Computer vision, Media BibRef

Hartley, T., Sidorov, K., Willis, C., Marshall, D.,
Explaining Failure: Investigation of Surprise and Expectation in CNNs,
TCV20(56-65)
IEEE DOI 2008
Training data, Training, Convolution, Data models, Convolutional neural networks, Data visualization, Mathematical model BibRef

Ramanujan, V., Wortsman, M., Kembhavi, A., Farhadi, A., Rastegari, M.,
What's Hidden in a Randomly Weighted Neural Network?,
CVPR20(11890-11899)
IEEE DOI 2008
Training, Neurons, Biological neural networks, Stochastic processes, Buildings, Standards BibRef

Bansal, N.[Naman], Agarwal, C.[Chirag], Nguyen, A.[Anh],
SAM: The Sensitivity of Attribution Methods to Hyperparameters,
CVPR20(8670-8680)
IEEE DOI 2008
BibRef
And: TCV20(11-21)
IEEE DOI 2008
Robustness, Sensitivity, Heating systems, Noise measurement, Limiting, Smoothing methods BibRef

Wang, H., Wu, X., Huang, Z., Xing, E.P.,
High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks,
CVPR20(8681-8691)
IEEE DOI 2008
Training, Robustness, Hybrid fiber coaxial cables, Mathematical model, Convolutional neural networks, Data models BibRef

Wu, W., Su, Y., Chen, X., Zhao, S., King, I., Lyu, M.R., Tai, Y.,
Towards Global Explanations of Convolutional Neural Networks With Concept Attribution,
CVPR20(8649-8658)
IEEE DOI 2008
Feature extraction, Predictive models, Detectors, Cognition, Semantics, Neurons, Computational modeling BibRef

Agarwal, A., Singh, R., Vatsa, M.,
The Role of 'Sign' and 'Direction' of Gradient on the Performance of CNN,
WMF20(2748-2756)
IEEE DOI 2008
Databases, Machine learning, Computational modeling, Object recognition, Computer vision, Training, Optimization BibRef

Wang, H., Wang, Z., Du, M., Yang, F., Zhang, Z., Ding, S., Mardziel, P., Hu, X.,
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks,
TCV20(111-119)
IEEE DOI 2008
Visualization, Convolution, Noise measurement, Convolutional neural networks, Task analysis, Debugging, Tools BibRef

Baik, S., Hong, S., Lee, K.M.,
Learning to Forget for Meta-Learning,
CVPR20(2376-2384)
IEEE DOI 2008
Task analysis, Attenuation, Adaptation models, Optimization, Training, Neural networks, Loss measurement BibRef

Zhang, Z., Lathuilière, S., Ricci, E., Sebe, N., Yan, Y., Yang, J.,
Online Depth Learning Against Forgetting in Monocular Videos,
CVPR20(4493-4502)
IEEE DOI 2008
Adaptation models, Videos, Estimation, Task analysis, Robustness, Machine learning, Training BibRef

Davidson, G., Mozer, M.C.,
Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget,
CVPR20(9279-9290)
IEEE DOI 2008
Task analysis, Training, Visualization, Standards, Neural networks, Color, Interference BibRef

Wang, D., Li, Y., Wang, L., Gong, B.,
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation From a Blackbox Model,
CVPR20(1495-1504)
IEEE DOI 2008
Neural networks, Computational modeling, Data models, Training, Knowledge engineering, Visualization, Manifolds BibRef

Masarczyk, W., Tautkute, I.,
Reducing catastrophic forgetting with learning on synthetic data,
CLVision20(1019-1024)
IEEE DOI 2008
Task analysis, Optimization, Generators, Data models, Neural networks, Training, Computer architecture BibRef

Kim, E., Gopinath, D., Pasareanu, C., Seshia, S.A.,
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors,
CVPR20(11125-11134)
IEEE DOI 2008
Semantics, Automobiles, Feature extraction, Detectors, Probabilistic logic, Debugging, Computer vision BibRef

Golatkar, A., Achille, A., Soatto, S.,
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks,
CVPR20(9301-9309)
IEEE DOI 2008
Training, Neural networks, Data models, Stochastic processes, Task analysis, Training data BibRef

Jalwana, M.A.A.K.[M. A. A. K.], Akhtar, N., Bennamoun, M., Mian, A.,
Attack to Explain Deep Representation,
CVPR20(9540-9549)
IEEE DOI 2008
Perturbation methods, Computational modeling, Visualization, Robustness, Image generation, Machine learning, Task analysis BibRef

Koperski, M.[Michal], Konopczynski, T.[Tomasz], Nowak, R.[Rafal], Semberecki, P.[Piotr], Trzcinski, T.[Tomasz],
Plugin Networks for Inference under Partial Evidence,
WACV20(2872-2880)
IEEE DOI 2006
Plugin layers to pre-trained network. Task analysis, Training, Visualization, Neural networks, Image segmentation, Image annotation, Image recognition BibRef

Chen, L., Chen, J., Hajimirsadeghi, H., Mori, G.,
Adapting Grad-CAM for Embedding Networks,
WACV20(2783-2792)
IEEE DOI 2006
Visualization, Testing, Training, Databases, Estimation, Heating systems, Task analysis BibRef

Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.,
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks,
WACV18(839-847)
IEEE DOI 1806
computer vision, convolution, feedforward neural nets, gradient methods, learning (artificial intelligence), Visualization BibRef

Zhang, J., Zhang, J., Ghosh, S., Li, D., Tasci, S., Heck, L., Zhang, H., Kuo, C.C.J.[C.C. Jay],
Class-incremental Learning via Deep Model Consolidation,
WACV20(1120-1129)
IEEE DOI 2006
Data models, Task analysis, Training, Monte Carlo methods, Training data, Computational modeling, Adaptation models BibRef

Vasu, B., Long, C.,
Iterative and Adaptive Sampling with Spatial Attention for Black-Box Model Explanations,
WACV20(2949-2958)
IEEE DOI 2006
Adaptation models, Neural networks, Feature extraction, Mathematical model, Decision making, Machine learning, Visualization BibRef

Desai, S., Ramaswamy, H.G.,
Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization,
WACV20(972-980)
IEEE DOI 2006
Visualization, Neurons, Task analysis, Computer architecture, Data models, Data visualization, Backpropagation BibRef

Gkalelis, N.[Nikolaos], Mezaris, V.[Vasileios],
Subclass Deep Neural Networks: Re-enabling Neglected Classes in Deep Network Training for Multimedia Classification,
MMMod20(I:227-238).
Springer DOI 2003
BibRef

Patro, B.[Badri], Lunayach, M.[Mayank], Patel, S.[Shivansh], Namboodiri, V.[Vinay],
U-CAM: Visual Explanation Using Uncertainty Based Class Activation Maps,
ICCV19(7443-7452)
IEEE DOI 2004
inference mechanisms, learning (artificial intelligence), visual explanation, uncertainty based class activation maps, Data models BibRef

Wu, T., Song, X.,
Towards Interpretable Object Detection by Unfolding Latent Structures,
ICCV19(6032-6042)
IEEE DOI 2004
Code, Object Detection.
WWW Link. convolutional neural nets, grammars, learning (artificial intelligence), object detection, Predictive models BibRef

Sun, Y., Ravi, S., Singh, V.,
Adaptive Activation Thresholding: Dynamic Routing Type Behavior for Interpretability in Convolutional Neural Networks,
ICCV19(4937-4946)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), Standards BibRef

Michelini, P.N., Liu, H., Lu, Y., Jiang, X.,
A Tour of Convolutional Networks Guided by Linear Interpreters,
ICCV19(4752-4761)
IEEE DOI 2004
convolutional neural nets, image classification, image resolution, copy-move strategies, Switches BibRef

Shoshan, A.[Alon], Mechrez, R.[Roey], Zelnik-Manor, L.[Lihi],
Dynamic-Net: Tuning the Objective Without Re-Training for Synthesis Tasks,
ICCV19(3214-3222)
IEEE DOI 2004
convolutional neural nets, image processing, optimisation, Dynamic-Net, synthesis tasks, optimization, modern CNN, Face BibRef

Subramanya, A., Pillai, V., Pirsiavash, H.,
Fooling Network Interpretation in Image Classification,
ICCV19(2020-2029)
IEEE DOI 2004
decision making, image classification, learning (artificial intelligence), neural nets, Task analysis BibRef

Liang, M.[Megan], Palado, G.[Gabrielle], Browne, W.N.[Will N.],
Identifying Simple Shapes to Classify the Big Picture,
IVCNZ19(1-6)
IEEE DOI 2004
evolutionary computation, feature extraction, image classification, learning (artificial intelligence), Learning Classifier Systems BibRef

Yin, B., Tran, L., Li, H., Shen, X., Liu, X.,
Towards Interpretable Face Recognition,
ICCV19(9347-9356)
IEEE DOI 2004
convolutional neural nets, face recognition, feature extraction, image representation, learning (artificial intelligence), Feature extraction BibRef

O'Neill, D., Xue, B., Zhang, M.,
The Evolution of Adjacency Matrices for Sparsity of Connection in DenseNets,
IVCNZ19(1-6)
IEEE DOI 2004
convolutional neural nets, genetic algorithms, image classification, matrix algebra, image classification, reduced model complexity BibRef

Sulc, M., Matas, J.,
Improving CNN Classifiers by Estimating Test-Time Priors,
TASKCV19(3220-3226)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), maximum likelihood estimation, pattern classification, Probabilistic Classifiers BibRef

Huang, J., Qu, L., Jia, R., Zhao, B.,
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks,
ICCV19(3325-3333)
IEEE DOI 2004
computer vision, learning (artificial intelligence), neural nets, probability, deep neural networks, human annotations, BibRef

Lee, K., Lee, K., Shin, J., Lee, H.,
Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild,
ICCV19(312-321)
IEEE DOI 2004
Code, Neural Networks.
WWW Link. image sampling, learning (artificial intelligence), neural nets, distillation loss, global distillation, learning strategy, Neural networks BibRef

Konuk, E., Smith, K.,
An Empirical Study of the Relation Between Network Architecture and Complexity,
Preregister19(4597-4599)
IEEE DOI 2004
generalisation (artificial intelligence), image classification, network architecture, preregistration submission, complexity BibRef

Navarrete Michelini, P., Liu, H., Lu, Y., Jiang, X.,
Understanding Convolutional Networks Using Linear Interpreters - Extended Abstract,
VXAI19(4186-4189)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, image resolution, image segmentation, deep-learning BibRef

Iqbal, A., Gall, J.,
Level Selector Network for Optimizing Accuracy-Specificity Trade-Offs,
HVU19(1466-1473)
IEEE DOI 2004
directed graphs, image processing, learning (artificial intelligence), video signal processing, Deep Learning BibRef

Lee, H., Kim, H., Nam, H.,
SRM: A Style-Based Recalibration Module for Convolutional Neural Networks,
ICCV19(1854-1862)
IEEE DOI 2004
calibration, convolutional neural nets, feature extraction, image recognition, image representation, Training BibRef

Chen, R., Chen, H., Huang, G., Ren, J., Zhang, Q.,
Explaining Neural Networks Semantically and Quantitatively,
ICCV19(9186-9195)
IEEE DOI 2004
convolutional neural nets, image processing, learning (artificial intelligence), semantic explanation, Task analysis BibRef

Stergiou, A., Kapidis, G., Kalliatakis, G., Chrysoulas, C., Poppe, R., Veltkamp, R.,
Class Feature Pyramids for Video Explanation,
VXAI19(4255-4264)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, image motion analysis, saliency-visualization BibRef

Kang, S., Jung, H., Lee, S.,
Interpreting Undesirable Pixels for Image Classification on Black-Box Models,
VXAI19(4250-4254)
IEEE DOI 2004
data visualisation, explanation, image classification, image segmentation, neural nets, neural networks, Interpretability BibRef

Zhuang, J., Dvornek, N.C., Li, X., Yang, J., Duncan, J.,
Decision explanation and feature importance for invertible networks,
VXAI19(4235-4239)
IEEE DOI 2004
Code, Neural Networks.
WWW Link. neural nets, pattern classification, linear classifier, feature space, decision boundary, feature importance, Decision-Boundary BibRef

Yoon, J., Kim, K., Jang, J.,
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation,
VXAI19(4226-4234)
IEEE DOI 2004
convolutional neural nets, image classification, image denoising, learning (artificial intelligence), cosine distance, adversarial-attack BibRef

Parafita, Á., Vitrià, J.,
Explaining Visual Models by Causal Attribution,
VXAI19(4167-4175)
IEEE DOI 2004
data handling, feature extraction, intervened causal model, causal attribution, visual models, image generative models, learning BibRef

Schlegel, U., Arnout, H., El-Assady, M., Oelke, D., Keim, D.A.,
Towards A Rigorous Evaluation Of XAI Methods On Time Series,
VXAI19(4197-4201)
IEEE DOI 2004
image processing, learning (artificial intelligence), text analysis, time series, SHAP, image domain, text-domain, explainable-ai-evaluation BibRef

Marcos, D., Lobry, S., Tuia, D.,
Semantically Interpretable Activation Maps: what-where-how explanations within CNNs,
VXAI19(4207-4215)
IEEE DOI 2004
convolutional neural nets, image classification, learning (artificial intelligence), attributes BibRef

Graziani, M.[Mara], Müller, H.[Henning], Andrearczyk, V.[Vincent], Graziani, M., Müller, H., Andrearczyk, V.,
Interpreting Intentionally Flawed Models with Linear Probes,
SDL-CV19(743-747)
IEEE DOI 2004
learning (artificial intelligence), pattern classification, regression analysis, statistical irregularities, regression, linear probes BibRef

Demidovskij, A., Gorbachev, Y., Fedorov, M., Slavutin, I., Tugarev, A., Fatekhov, M., Tarkan, Y.,
OpenVINO Deep Learning Workbench: Comprehensive Analysis and Tuning of Neural Networks Inference,
SDL-CV19(783-787)
IEEE DOI 2004
interactive systems, learning (artificial intelligence), neural nets, optimisation, user interfaces, hyper parameters, optimization BibRef

Iwana, B.K., Kuroki, R., Uchida, S.,
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation,
VXAI19(4176-4185)
IEEE DOI 2004
convolutional neural nets, data visualisation, image classification, image representation, probability, SGLRP, explainability BibRef

Lazarow, J., Jin, L., Tu, Z.,
Introspective Neural Networks for Generative Modeling,
ICCV17(2793-2802)
IEEE DOI 1802
image classification, image representation, image texture, neural nets, neurocontrollers, statistics, unsupervised learning, Training 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

Ren, J.[Jian], Li, Z.[Zhe], Yang, J.C.[Jian-Chao], Xu, N.[Ning], Yang, T.[Tianbao], Foran, D.J.[David J.],
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching From Scratch,
CVPR19(9051-9060).
IEEE DOI 2002
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Liang, X.D.[Xiao-Dan],
Learning Personalized Modular Network Guided by Structured Knowledge,
CVPR19(8936-8944).
IEEE DOI 2002
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Zeng, W.Y.[Wen-Yuan], Luo, W.J.[Wen-Jie], Suo, S.[Simon], Sadat, A.[Abbas], Yang, B.[Bin], Casas, S.[Sergio], Urtasun, R.[Raquel],
End-To-End Interpretable Neural Motion Planner,
CVPR19(8652-8661).
IEEE DOI 2002
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Blanchard, N.[Nathaniel], Kinnison, J.[Jeffery], RichardWebster, B.[Brandon], Bashivan, P.[Pouya], Scheirer, W.J.[Walter J.],
A Neurobiological Evaluation Metric for Neural Network Model Search,
CVPR19(5399-5408).
IEEE DOI 2002
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Yu, L.[Lu], Yazici, V.O.[Vacit Oguz], Liu, X.L.[Xia-Lei], van de Weijer, J.[Joost], Cheng, Y.M.[Yong-Mei], Ramisa, A.[Arnau],
Learning Metrics From Teachers: Compact Networks for Image Embedding,
CVPR19(2902-2911).
IEEE DOI 2002
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Ye, J.W.[Jing-Wen], Ji, Y.X.[Yi-Xin], Wang, X.C.[Xin-Chao], Ou, K.[Kairi], Tao, D.P.[Da-Peng], Song, M.L.[Ming-Li],
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More,
CVPR19(2824-2833).
IEEE DOI 2002
Train one model that combines the knowledge of 2 other trained nets. BibRef

Zhang, Q.S.[Quan-Shi], Yang, Y.[Yu], Ma, H.[Haotian], Wu, Y.N.[Ying Nian],
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Knockoff Nets: Stealing Functionality of Black-Box Models,
CVPR19(4949-4958).
IEEE DOI 2002
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Morgado, P.[Pedro], Vasconcelos, N.M.[Nuno M.],
NetTailor: Tuning the Architecture, Not Just the Weights,
CVPR19(3039-3049).
IEEE DOI 2002
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Kwon, G.[Gukyeong], Prabhushankar, M.[Mohit], Temel, D.[Dogancan], Al Regib, G.[Ghassan],
Distorted Representation Space Characterization Through Backpropagated Gradients,
ICIP19(2651-2655)
IEEE DOI 1910
Gradients, Representation Learning, Out-of-distribution, Image Quality Assessment, Autoencoder BibRef

Stergiou, A.[Alexandros], Kapidis, G.[Georgios], Kalliatakis, G.[Grigorios], Chrysoulas, C.[Christos], Veltkamp, R.[Remco], Poppe, R.[Ronald],
Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions,
ICIP19(1830-1834)
IEEE DOI 1910
3-D convolutions. How to visualize the results. Visual explanations, explainable convolutions, spatio-temporal feature representation BibRef

Rao, Z., He, M., Zhu, Z.,
Input-Perturbation-Sensitivity for Performance Analysis of CNNS on Image Recognition,
ICIP19(2496-2500)
IEEE DOI 1910
Global Sensitivity Analysis, Convolutional Neural Networks, Quality, Image Classification BibRef

Chen, Y., Saporta, A., Dapogny, A., Cord, M.,
Delving Deep into Interpreting Neural Nets with Piece-Wise Affine Representation,
ICIP19(609-613)
IEEE DOI 1910
Deep learning, deep neural networks, attribution, pixel contribution, bias BibRef

Lee, J., Kim, S.T., Ro, Y.M.,
Probenet: Probing Deep Networks,
ICIP19(3821-3825)
IEEE DOI 1910
ProbeNet, Deep network probing, Deep network interpretation, Human-understandable BibRef

Buhrmester, V.[Vanessa], Münch, D.[David], Bulatov, D.[Dimitri], Arens, M.[Michael],
Evaluating the Impact of Color Information in Deep Neural Networks,
IbPRIA19(I:302-316).
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Geometric Interpretation of CNNs' Last Layer,
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Towards a Joint Approach to Produce Decisions and Explanations Using CNNs,
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Behavior-Based Compression for Convolutional Neural Networks,
ICIAR19(I:427-439).
Springer DOI 1909
Reducing redundancy. BibRef

Tartaglione, E.[Enzo], Grangetto, M.[Marco],
Take a Ramble into Solution Spaces for Classification Problems in Neural Networks,
CIAP19(I:345-355).
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Gu, J.D.[Jin-Dong], Yang, Y.C.[Yin-Chong], Tresp, V.[Volker],
Understanding Individual Decisions of CNNs via Contrastive Backpropagation,
ACCV18(III:119-134).
Springer DOI 1906
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Yu, T.[Tao], Long, H.[Huan], Hopcroft, J.E.[John E.],
Curvature-based Comparison of Two Neural Networks,
ICPR18(441-447)
IEEE DOI 1812
Manifolds, Biological neural networks, Tensile stress, Measurement, Matrix decomposition, Covariance matrices BibRef

Malakhova, K.[Katerina],
Representation of Categories in Filters of Deep Neural Networks,
Cognitive18(2054-20542)
IEEE DOI 1812
Visualization, Face, Feature extraction, Detectors, Biological neural networks, Neurons, Automobiles BibRef

Kanbak, C.[Can], Moosavi-Dezfooli, S.M.[Seyed-Mohsen], Frossard, P.[Pascal],
Geometric Robustness of Deep Networks: Analysis and Improvement,
CVPR18(4441-4449)
IEEE DOI 1812
Robustness, Manifolds, Additives, Training, Atmospheric measurements, Particle measurements BibRef

Shmelkov, K., Schmid, C., Alahari, K.,
Incremental Learning of Object Detectors without Catastrophic Forgetting,
ICCV17(3420-3429)
IEEE DOI 1802
learning (artificial intelligence), neural nets, object detection, COCO datasets, PASCAL VOC 2007, annotations, Training data BibRef

Rannen, A.[Amal], Aljundi, R.[Rahaf], Blaschko, M.B.[Matthew B.], Tuytelaars, T.[Tinne],
Encoder Based Lifelong Learning,
ICCV17(1329-1337)
IEEE DOI 1802
Learning usually adapts to the most recent task, need a sequence of tasks. feature extraction, image classification, learning (artificial intelligence), catastrophic forgetting, Training BibRef

Aljundi, R.[Rahaf], Babiloni, F.[Francesca], Elhoseiny, M.[Mohamed], Rohrbach, M.[Marcus], Tuytelaars, T.[Tinne],
Memory Aware Synapses: Learning What (not) to Forget,
ECCV18(III: 144-161).
Springer DOI 1810
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Liu, X.L.[Xia-Lei], Masana, M., Herranz, L., van de Weijer, J.[Joost], López, A.M., Bagdanov, A.D.[Andrew D.],
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting,
ICPR18(2262-2268)
IEEE DOI 1812
Task analysis, Training, Training data, Neural networks, Data models, Computer vision, Standards BibRef

Fawzi, A., Moosavi-Dezfooli, S., Frossard, P., Soatto, S.,
Empirical Study of the Topology and Geometry of Deep Networks,
CVPR18(3762-3770)
IEEE DOI 1812
Neural networks, Perturbation methods, Geometry, Network topology, Topology, Robustness, Optimization BibRef

Zhang, Z.M.[Zi-Ming], Wu, Y.W.[Yuan-Wei], Wang, G.H.[Guang-Hui],
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning,
CVPR18(3301-3309)
IEEE DOI 1812
Optimization, Linear programming, Upper bound, Approximation algorithms, Biological neural networks, Convergence BibRef

Palacio, S., Folz, J., Hees, J., Raue, F., Borth, D., Dengel, A.,
What do Deep Networks Like to See?,
CVPR18(3108-3117)
IEEE DOI 1812
Image reconstruction, Training, Neural networks, Decoding, Task analysis, Convolution, Image coding BibRef

Aodha, O.M., Su, S., Chen, Y., Perona, P., Yue, Y.,
Teaching Categories to Human Learners with Visual Explanations,
CVPR18(3820-3828)
IEEE DOI 1812
Education, Visualization, Task analysis, Adaptation models, Mathematical model, Computational modeling, Computer vision BibRef

Fong, R., Vedaldi, A.,
Net2Vec: Quantifying and Explaining How Concepts are Encoded by Filters in Deep Neural Networks,
CVPR18(8730-8738)
IEEE DOI 1812
Semantics, Visualization, Image segmentation, Probes, Neural networks, Task analysis, Training BibRef

Mascharka, D., Tran, P., Soklaski, R., Majumdar, A.,
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning,
CVPR18(4942-4950)
IEEE DOI 1812
Visualization, Cognition, Task analysis, Neural networks, Image color analysis, Knowledge discovery, Automobiles BibRef

Wang, Y., Su, H., Zhang, B., Hu, X.,
Interpret Neural Networks by Identifying Critical Data Routing Paths,
CVPR18(8906-8914)
IEEE DOI 1812
Routing, Logic gates, Neural networks, Predictive models, Encoding, Semantics, Analytical models BibRef

Dong, Y.P.[Yin-Peng], Su, H.[Hang], Zhu, J.[Jun], Zhang, B.[Bo],
Improving Interpretability of Deep Neural Networks with Semantic Information,
CVPR17(975-983)
IEEE DOI 1711
Computational modeling, Decoding, Feature extraction, Neurons, Semantics, Visualization BibRef

Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.B.,
Network Dissection: Quantifying Interpretability of Deep Visual Representations,
CVPR17(3319-3327)
IEEE DOI 1711
Detectors, Image color analysis, Image segmentation, Semantics, Training, Visualization BibRef

Hu, R.H.[Rong-Hang], Andreas, J.[Jacob], Darrell, T.J.[Trevor J.], Saenko, K.[Kate],
Explainable Neural Computation via Stack Neural Module Networks,
ECCV18(VII: 55-71).
Springer DOI 1810
BibRef

Rupprecht, C., Laina, I., Navab, N., Hager, G.D., Tombari, F.,
Guide Me: Interacting with Deep Networks,
CVPR18(8551-8561)
IEEE DOI 1812
Image segmentation, Visualization, Natural languages, Task analysis, Semantics, Head, Training BibRef

Zhang, Q., Wu, Y.N., Zhu, S.,
Interpretable Convolutional Neural Networks,
CVPR18(8827-8836)
IEEE DOI 1812
Visualization, Semantics, Integrated circuits, Convolutional neural networks, Task analysis, Training, Entropy BibRef

Khan, S.H.[Salman H.], Hayat, M.[Munawar], Porikli, F.M.[Fatih Murat],
Scene Categorization with Spectral Features,
ICCV17(5639-5649)
IEEE DOI 1802
Explain the network results. feature extraction, image classification, image representation, learning (artificial intelligence), natural scenes, transforms, Transforms BibRef

Worrall, D.E.[Daniel E.], Garbin, S.J.[Stephan J.], Turmukhambetov, D.[Daniyar], Brostow, G.J.[Gabriel J.],
Interpretable Transformations with Encoder-Decoder Networks,
ICCV17(5737-5746)
IEEE DOI 1802
I.e. rotation effects. Explain results. decoding, image coding, interpolation, transforms, complex transformation encoding, BibRef

Sankaranarayanan, S.[Swami], Jain, A.[Arpit], Lim, S.N.[Ser Nam],
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks,
ICCV17(3582-3590)
IEEE DOI 1802
Perturb the inputs, understand NN results. Explain. image classification, image representation, neural nets, CIFAR10 datasets, MNIST, PASCAL VOC dataset, Semantics BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Convolutional Neural Networks for Object Detection and Segmentation .


Last update:Sep 21, 2020 at 13:40:48