14.5.10.8.12 CNN Intrepretation, Explanation, Understanding of Convolutional Neural Networks

Chapter Contents (Back)
Convolutional Neural Networks. Explanable. CNN.
See also Intrepretation, Explanation, Neural Networks Deep Neural Networks.
See also Forgetting, Learning without Forgetting, Convolutional Neural Networks.
See also Continual Learning.
See also Dynamic Learning, Incremental Learning.
See also Knowledge Distillation. General Explanable:
See also Explainable Aritficial Intelligence.

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
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.Y.[Si-Yang], Duan, J.L.[Jia-Li], 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.K.[Yun-Ke], 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

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

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.Q.[Li-Qiang], Guo, B.Q.[Bao-Qing],
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

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

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

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

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

Shi, X., Xing, F., Xu, K., Chen, P., Liang, Y., Lu, Z., Guo, Z.,
Loss-Based Attention for Interpreting Image-Level Prediction of Convolutional Neural Networks,
IP(30), 2021, pp. 1662-1675.
IEEE DOI 2101
Feature extraction, Routing, Visualization, Training, Convolutional codes, weighted sum BibRef

Gu, R.[Ran], Wang, G.T.[Guo-Tai], Song, T.[Tao], Huang, R.[Rui], Aertsen, M.[Michael], Deprest, J.[Jan], Ourselin, S.[Sébastien], Vercauteren, T.[Tom], Zhang, S.T.[Shao-Ting],
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation,
MedImg(40), No. 2, February 2021, pp. 699-711.
IEEE DOI 2102
Image segmentation, Task analysis, Feature extraction, Medical diagnostic imaging, Shape, Convolutional neural networks, explainability BibRef

Shin, S.[Sunguk], Kim, Y.J.[Young-Joon], Yoon, J.W.[Ji Won],
A new approach to training more interpretable model with additional segmentation,
PRL(152), 2021, pp. 188-194.
Elsevier DOI 2112
Classification model, Convolutional neural networks, Interpretable machine learning BibRef

Feng, Z.P.[Zhen-Peng], Zhu, M.Z.[Ming-Zhe], Stankovic, L.[Ljubiša], Ji, H.B.[Hong-Bing],
Self-Matching CAM: A Novel Accurate Visual Explanation of CNNs for SAR Image Interpretation,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Wang, D.[Dan], Cui, X.R.[Xin-Rui], Chen, X.[Xun], Ward, R.[Rabab], Wang, Z.J.[Z. Jane],
Interpreting Bottom-Up Decision-Making of CNNs via Hierarchical Inference,
IP(30), 2021, pp. 6701-6714.
IEEE DOI 2108
Visualization, Decision making, Semantics, Image color analysis, Perturbation methods, Neuroscience, Training, Interpretation model, decision-making process BibRef

Zhang, Q.S.[Quan-Shi], Wang, X.[Xin], Wu, Y.N.[Ying Nian], Zhou, H.L.[Hui-Lin], Zhu, S.C.[Song-Chun],
Interpretable CNNs for Object Classification,
PAMI(43), No. 10, October 2021, pp. 3416-3431.
IEEE DOI 2109
Visualization, Semantics, Neural networks, Task analysis, Feature extraction, Annotations, Benchmark testing, interpretable deep learning BibRef

Zhang, Q.S.[Quan-Shi], Wang, X.[Xin], Cao, R.M.[Rui-Ming], Wu, Y.N.[Ying Nian], Shi, F.[Feng], Zhu, S.C.[Song-Chun],
Extraction of an Explanatory Graph to Interpret a CNN,
PAMI(43), No. 11, November 2021, pp. 3863-3877.
IEEE DOI 2110
Feature extraction, Visualization, Neural networks, Semantics, Annotations, Task analysis, Training, interpretable deep learning BibRef

Cheng, L.[Lin], Fang, P.F.[Peng-Fei], Liang, Y.J.[Yan-Jie], Zhang, L.[Liao], Shen, C.H.[Chun-Hua], Wang, H.Z.[Han-Zi],
TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency,
IP(31), 2022, pp. 2529-2540.
IEEE DOI 2204
Visualization, Semantics, Task analysis, Convolutional neural networks, Medical diagnostic imaging, CNN visualization BibRef

Muddamsetty, S.M.[Satya M.], Jahromi, M.N.S.[Mohammad N.S.], Ciontos, A.E.[Andreea E.], Fenoy, L.M.[Laura M.], Moeslund, T.B.[Thomas B.],
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method,
PR(127), 2022, pp. 108604.
Elsevier DOI 2205
Explainable AI (XAI), CNN, Adversarial attack, Eye-tracker BibRef

Huang, Z.L.[Zhong-Ling], Yao, X.[Xiwen], Liu, Y.[Ying], Dumitru, C.O.[Corneliu Octavian], Datcu, M.[Mihai], Han, J.W.[Jun-Wei],
Physically explainable CNN for SAR image classification,
PandRS(190), 2022, pp. 25-37.
Elsevier DOI 2208
Explainable deep learning, Physical model, SAR image classification, Prior knowledge BibRef

Yuan, H.[Hao], Cai, L.[Lei], Hu, X.[Xia], Wang, J.[Jie], Ji, S.W.[Shui-Wang],
Interpreting Image Classifiers by Generating Discrete Masks,
PAMI(44), No. 4, April 2022, pp. 2019-2030.
IEEE DOI 2203
Generators, Predictive models, Training, Computational modeling, Neurons, Convolutional neural networks, image classification, reinforcement learning BibRef

Shi, R.[Rui], Li, T.X.[Tian-Xing], Yamaguchi, Y.S.[Yasu-Shi],
Output-targeted baseline for neuron attribution calculation,
IVC(124), 2022, pp. 104516.
Elsevier DOI 2208
Convolutional neural networks, Network interpretability, Attribution methods, Shapley values BibRef

Guo, X.P.[Xian-Peng], Hou, B.[Biao], Wu, Z.T.[Zi-Tong], Ren, B.[Bo], Wang, S.[Shuang], Jiao, L.C.[Li-Cheng],
Prob-POS: A Framework for Improving Visual Explanations from Convolutional Neural Networks for Remote Sensing Image Classification,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Böhle, M.[Moritz], Fritz, M.[Mario], Schiele, B.[Bernt],
Optimising for Interpretability: Convolutional Dynamic Alignment Networks,
PAMI(45), No. 6, June 2023, pp. 7625-7638.
IEEE DOI 2305
Computational modeling, Neural networks, Predictive models, Informatics, Task analysis, Transforms, Ear, explainability in deep learning BibRef

Hu, K.W.[Kai-Wen], Gao, J.[Jing], Mao, F.Y.[Fang-Yuan], Song, X.H.[Xin-Hui], Cheng, L.C.[Le-Chao], Feng, Z.L.[Zun-Lei], Song, M.L.[Ming-Li],
Disassembling Convolutional Segmentation Network,
IJCV(131), No. 7, July 2023, pp. 1741-1760.
Springer DOI 2307
BibRef

Li, J.[Jing], Zhang, D.B.[Dong-Bo], Meng, B.[Bumin], Li, Y.X.[Yong-Xing], Luo, L.[Lufeng],
FIMF score-CAM: Fast score-CAM based on local multi-feature integration for visual interpretation of CNNS,
IET-IPR(17), No. 3, 2023, pp. 761-772.
DOI Link 2303
class activation mapping, deep network, model interpretation BibRef

Cheng, M.M.[Ming-Ming], Jiang, P.T.[Peng-Tao], Han, L.H.[Ling-Hao], Wang, L.[Liang], Torr, P.H.S.[Philip H.S.],
Deeply Explain CNN Via Hierarchical Decomposition,
IJCV(131), No. 5, May 2023, pp. 1091-1105.
Springer DOI 2305
BibRef

Böhle, M.[Moritz], Singh, N.[Navdeeppal], Fritz, M.[Mario], Schiele, B.[Bernt],
B-Cos Alignment for Inherently Interpretable CNNs and Vision Transformers,
PAMI(46), No. 6, June 2024, pp. 4504-4518.
IEEE DOI 2405
Computational modeling, Task analysis, Optimization, Visualization, Transformers, Training, Measurement, Convolutional neural networks, XAI BibRef

Islam, M.A.[Md Amirul], Kowal, M.[Matthew], Jia, S.[Sen], Derpanis, K.G.[Konstantinos G.], Bruce, N.D.B.[Neil D. B.],
Position, Padding and Predictions: A Deeper Look at Position Information in CNNs,
IJCV(132), No. 1, January 2024, pp. 3889-3910.
Springer DOI 2409
BibRef
Earlier:
Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs,
ICCV21(773-781)
IEEE DOI 2203
Tensors, Semantics, Neurons, Linear programming, Encoding, Object recognition, Explainable AI, Adversarial learning BibRef

Li, Y.[Yanshan], Liang, H.[Huajie], Yu, R.[Rui],
BI-CAM: Generating Explanations for Deep Neural Networks Using Bipolar Information,
MultMed(26), 2024, pp. 568-580.
IEEE DOI 2402
Neural networks, Feature extraction, Convolutional neural networks, Mutual information, Visualization, point-wise mutual information (PMI) BibRef

Tang, J.C.[Jia-Cheng], Kang, Q.[Qi], Zhou, M.C.[Meng-Chu], Yin, H.[Hao], Yao, S.[Siya],
MemeNet: Toward a Reliable Local Projection for Image Recognition via Semantic Featurization,
IP(33), 2024, pp. 1670-1682.
IEEE DOI 2403
Feature extraction, Reliability, Task analysis, Convolutional neural networks, Semantics, Image recognition, trustworthy machine learning BibRef

Kim, S.[Seonggyeom], Chae, D.K.[Dong-Kyu],
What Does a Model Really Look at?: Extracting Model-Oriented Concepts for Explaining Deep Neural Networks,
PAMI(46), No. 7, July 2024, pp. 4612-4624.
IEEE DOI 2406
Annotations, Image segmentation, Computational modeling, Predictive models, Convolutional neural networks, Crops, explainable AI BibRef

Rodrigues, C.M.[Caroline Mazini], Boutry, N.[Nicolas], Najman, L.[Laurent],
Transforming gradient-based techniques into interpretable methods,
PRL(184), 2024, pp. 66-73.
Elsevier DOI 2408
Explainable artificial intelligence, Convolutional Neural Network, Gradient-based, Interpretability BibRef


Jiang, M.Q.[Ming-Qi], Khorram, S.[Saeed], Fuxin, L.[Li],
Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods,
CVPR24(9546-9555)
IEEE DOI 2410
Visualization, Analytical models, Computational modeling, Decision making, Buildings, Transformers, Explanation, CNNs, Normalization BibRef

Alami, A.[Amine], Boumhidi, J.[Jaouad], Chakir, L.[Loqman],
Explainability in CNN based Deep Learning models for medical image classification,
ISCV24(1-6)
IEEE DOI 2408
Deep learning, Uncertainty, Pneumonia, Explainable AI, Computational modeling, Decision making, Feature extraction, Grad-CAM. BibRef

Akpudo, U.E.[Ugochukwu Ejike], Yu, X.H.[Xiao-Han], Zhou, J.[Jun], Gao, Y.S.[Yong-Sheng],
NCAF: NTD-based Concept Activation Factorisation Framework for CNN Explainability,
IVCNZ23(1-6)
IEEE DOI 2403
Visualization, Closed box, Dogs, Convolutional neural networks, Task analysis, Image reconstruction, Diseases, Explainability, non-negative Tucker decomposition BibRef

Meynen, T.[Toon], Behzadi-Khormouji, H.[Hamed], Oramas, J.[José],
Interpreting Convolutional Neural Networks by Explaining Their Predictions,
ICIP23(1685-1689)
IEEE DOI 2312
BibRef

Sarkar, S.[Soumyendu], Babu, A.R.[Ashwin Ramesh], Mousavi, S.[Sajad], Ghorbanpour, S.[Sahand], Gundecha, V.[Vineet], Guillen, A.[Antonio], Luna, R.[Ricardo], Naug, A.[Avisek],
RL-CAM: Visual Explanations for Convolutional Networks using Reinforcement Learning,
SAIAD23(3861-3869)
IEEE DOI 2309
BibRef

Zee, T.[Timothy], Lakshmana, M.[Manohar], Nwogu, I.[Ifeoma],
Towards Understanding the Behaviors of Pretrained Compressed Convolutional Models,
ICPR22(3450-3456)
IEEE DOI 2212
Location awareness, Visualization, Image coding, Quantization (signal), Graphics processing units, Feature extraction BibRef

Zheng, Q.[Quan], Wang, Z.W.[Zi-Wei], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Shap-CAM: Visual Explanations for Convolutional Neural Networks Based on Shapley Value,
ECCV22(XII:459-474).
Springer DOI 2211
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Salama, A.[Ahmed], Adly, N.[Noha], Torki, M.[Marwan],
Ablation-CAM++: Grouped Recursive Visual Explanations for Deep Convolutional Networks,
ICIP22(2011-2015)
IEEE DOI 2211
Measurement, Deep learning, Visualization, Focusing, Binary trees, Predictive models, Interpretable Models, Visual Explanations, Computer Vision BibRef

Wu, Y.X.[Yu-Xi], Chen, C.[Changhuai], Che, J.[Jun], Pu, S.L.[Shi-Liang],
FAM: Visual Explanations for the Feature Representations from Deep Convolutional Networks,
CVPR22(10297-10306)
IEEE DOI 2210
Representation learning, Visualization, Privacy, Ethics, Neurons, Feature extraction, privacy and ethics in vision, accountability, Recognition: detection BibRef

Yang, Y.[Yu], Kim, S.[Seungbae], Joo, J.[Jungseock],
Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention,
CVPR22(8323-8333)
IEEE DOI 2210
Training, Visualization, Machine vision, Computational modeling, Semantics, Training data, Explainable computer vision, Vision applications and systems BibRef

Gkartzonika, I.[Ioanna], Gkalelis, N.[Nikolaos], Mezaris, V.[Vasileios],
Learning Visual Explanations for DCNN-based Image Classifiers Using an Attention Mechanism,
Scarce22(396-411).
Springer DOI 2304
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Gupta, A.[Ankit], Sintorn, I.M.[Ida-Maria],
Towards Better Guided Attention and Human Knowledge Insertion in Deep Convolutional Neural Networks,
BioImage22(437-453).
Springer DOI 2304
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Uchiyama, T.[Tomoki], Sogi, N.[Naoya], Niinuma, K.[Koichiro], Fukui, K.[Kazuhiro],
Visually explaining 3D-CNN predictions for video classification with an adaptive occlusion sensitivity analysis,
WACV23(1513-1522)
IEEE DOI 2302
Sensitivity analysis, Shape, Volume measurement, Decision making, Extraterrestrial measurements, Computational efficiency BibRef

Li, H.[Hui], Li, Z.H.[Zi-Hao], Ma, R.[Rui], Wu, T.[Tieru],
FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs,
ICPR22(1300-1306)
IEEE DOI 2212
Visualization, Codes, Convolution, Perturbation methods, Switches, Prediction algorithms BibRef

Yadu, A.[Ankit], Suhas, P.K.[P K], Sinha, N.[Neelam],
Class Specific Interpretability in CNN Using Causal Analysis,
ICIP21(3702-3706)
IEEE DOI 2201
Measurement, Location awareness, Visualization, Image color analysis, Computational modeling, Machine learning, Machine Learning BibRef

Song, W.[Wei], Dai, S.Y.[Shu-Yuan], Huang, D.M.[Dong-Mei], Song, J.L.[Jin-Ling], Antonio, L.[Liotta],
Median-Pooling Grad-Cam: An Efficient Inference Level Visual Explanation for CNN Networks in Remote Sensing Image Classification,
MMMod21(II:134-146).
Springer DOI 2106
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Lam, P.C.H.[Peter Cho-Ho], Chu, L.[Lingyang], Torgonskiy, M.[Maxim], Pei, J.[Jian], Zhang, Y.[Yong], Wang, L.[Lanjun],
Finding Representative Interpretations on Convolutional Neural Networks,
ICCV21(1325-1334)
IEEE DOI 2203
Heating systems, Deep learning, Costs, Semantics, Convolutional neural networks, Explainable AI, BibRef

Abello, A.A.[Antonio A.], Hirata, R.[Roberto], Wang, Z.Y.[Zhang-Yang],
Dissecting the High-Frequency Bias in Convolutional Neural Networks,
UG21(863-871)
IEEE DOI 2109
Frequency conversion, Robustness, Frequency diversity BibRef

Jung, J.H.[Jay Hoon], Kwon, Y.M.[Young-Min],
Boundaries of Single-Class Regions in the Input Space of Piece-Wise Linear Neural Networks,
ICPR21(6027-6034)
IEEE DOI 2105
Linearity, Robustness, Convolutional neural networks, Nonlinear systems, Deep Neural Network BibRef

Liang, H.Y.[Hao-Yu], Ouyang, Z.H.[Zhi-Hao], Zeng, Y.Y.[Yu-Yuan], Su, H.[Hang], He, Z.H.[Zi-Hao], Xia, S.T.[Shu-Tao], Zhu, J.[Jun], Zhang, B.[Bo],
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters,
ECCV20(II:622-638).
Springer DOI 2011
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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

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

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

Gorokhovatskyi, O.[Oleksii], Peredrii, O.[Olena],
Recursive Division of Image for Explanation of Shallow CNN Models,
EDL-AI20(274-286).
Springer DOI 2103
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Konforti, Y.[Yael], Shpigler, A.[Alon], Lerner, B.[Boaz], Bar-Hillel, A.[Aharon],
Inference Graphs for CNN Interpretation,
ECCV20(XXV:69-84).
Springer DOI 2011
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Rombach, R.[Robin], Esser, P.[Patrick], Ommer, B.[Björn],
Making Sense of CNNs: Interpreting Deep Representations and Their Invariances with INNs,
ECCV20(XVII:647-664).
Springer DOI 2011
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Ye, J.W.[Jing-Wen], Ji, Y.X.[Yi-Xin], Wang, X.C.[Xin-Chao], 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

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

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

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, Training, Optimization 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, Data models, Data visualization, Backpropagation 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

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

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

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

Kamma, K.[Koji], Isoda, Y.[Yuki], Inoue, S.[Sarimu], Wada, T.[Toshikazu],
Behavior-Based Compression for Convolutional Neural Networks,
ICIAR19(I:427-439).
Springer DOI 1909
Reducing redundancy. 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

Sulc, M., Matas, J.G.,
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

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

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

Zhang, Q.S.[Quan-Shi], Yang, Y.[Yu], Ma, H.T.[Hao-Tian], Wu, Y.N.[Ying Nian],
Interpreting CNNs via Decision Trees,
CVPR19(6254-6263).
IEEE DOI 2002
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

de la Calle, A.[Alejandro], Tovar, J.[Javier], Almazán, E.J.[Emilio J.],
Geometric Interpretation of CNNs' Last Layer,
IbPRIA19(I:137-147).
Springer DOI 1910
BibRef

Rio-Torto, I.[Isabel], Fernandes, K.[Kelwin], Teixeira, L.F.[Luís F.],
Towards a Joint Approach to Produce Decisions and Explanations Using CNNs,
IbPRIA19(I:3-15).
Springer DOI 1910
BibRef

Pope, P.E.[Phillip E.], Kolouri, S.[Soheil], Rostami, M.[Mohammad], Martin, C.E.[Charles E.], Hoffmann, H.[Heiko],
Explainability Methods for Graph Convolutional Neural Networks,
CVPR19(10764-10773).
IEEE DOI 2002
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
convolution, feedforward neural nets, gradient methods, learning (artificial intelligence), Visualization BibRef

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
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

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
Forgetting, Learning without Forgetting, Convolutional Neural Networks .


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