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
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
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
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
BibRef
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
BibRef
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
BibRef
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 .