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Year Publication Paper Citation
2014 ECCV Visualizing and Understanding Convolutional Networks 8009
2016 KDD Why should i trust you?: Explaining the predictions of any classifier 2255
2014 ICLR Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps 2014
2015 ICLR Striving for Simplicity: The All Convolutional Net 1762
2017 ICCV Grad-cam: Visual explanations from deep networks via gradient-based localization 1333
2015 ICMLW Understanding Neural Networks Through Deep Visualization 974
2016 arxiv The Mythos of Model Interpretability 951
2015 CVPR Understanding deep image representations by inverting them 929
2017 NIPS A Unified Approach to Interpreting Model Predictions 591
2017 ICML Understanding Black-box Predictions via Influence Functions 517
2018 DSP Methods for interpreting and understanding deep neural networks(scihub) 469
2017 CVPR Knowing when to look: Adaptive attention via a visual sentinel for image captioning 458
2017 ICML Axiomatic attribution for deep networks 448
2017 CVPR Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering 393
2017 ICML Learning Important Features Through Propagating Activation Differences 383
2019 AI Explanation in artificial intelligence: Insights from the social sciences 380
2017 CVPR Network dissection: Quantifying interpretability of deep visual representations 373
2019 CSUR A Survey of Methods for Explaining Black Box Models 344
2016 NIPS Understanding the effective receptive field in deep convolutional neural networks 310
2015 AAS Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model 304
2017 ACMSOPP Deepxplore: Automated whitebox testing of deep learning systems 302
2017 ICCV Interpretable Explanations of Black Boxes by Meaningful Perturbation 284
2016 NAACL Visualizing and understanding neural models in nlp 269
2016 CVPR Inverting Visual Representations with Convolutional Networks 266
2018 IJCV Top-down neural attention by excitation backprop 256
2016 NIPS Synthesizing the preferred inputs for neurons in neural networks via deep generator networks 251
2016 EMNLP Rationalizing Neural Predictions 247
2016 ECCV Generating Visual Explanations 224
2016 ICML Understanding and improving convolutional neural networks via concatenated rectified linear units 216
2016 IJCV Visualizing deep convolutional neural networks using natural pre-images 216
2017 ICLR Visualizing deep neural network decisions: Prediction difference analysis 212
2017 arxiv SmoothGrad: removing noise by adding noise 212
2017 arxiv Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models 210
2018 AAAI Anchors: High-precision model-agnostic explanations 200
2016 TVCG Towards better analysis of deep convolutional neural networks 184
2018 ECCV Deep clustering for unsupervised learning of visual features 167
2018 CVPR Interpretable Convolutional Neural Networks 154
2018 FITEE Visual interpretability for deep learning: a survey 140
2016 arxiv Understanding neural networks through representation erasure 137
2018 Access Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI) 131
2016 arxiv Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks 130
2017 arxiv Distilling a neural network into a soft decision tree 126
2018 ICLR Towards better understanding of gradient-based attribution methods for deep neural networks 123
2018 NIPS Sanity Checks for Saliency Maps 122
2016 TVCG Visualizing the hidden activity of artificial neural networks 122
2017 TVCG ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models 113
2018 AAAI Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients 112
2017 NIPS Real time image saliency for black box classifiers 111
2018 ICML Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav) 110
2017 CVPR Interpretable 3d human action analysis with temporal convolutional networks 106
2017 IJCAI Right for the right reasons: Training differentiable models by constraining their explanations 102
2017 NIPS SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability 97
2019 ExplainAI The (Un)reliability of saliency methods(scihub) 95
2015 ICCV Understanding deep features with computer-generated imagery 94
2018 ICLR Learning how to explain neural networks: PatternNet and PatternAttribution 90
2018 JAIR Learning Explanatory Rules from Noisy Data 90
2016 arxiv Grad-CAM: Why did you say that? 87
2017 CVPR MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network 86
2018 WACV Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks 85
2016 CVPR Visualizing and Understanding Deep Texture Representations 83
2016 CVPR Analyzing Classifiers: Fisher Vectors and Deep Neural Networks 82
2018 ICLR On the importance of single directions for generalization 81
2018 CVPR Tell me where to look: Guided attention inference network 81
2017 ICCV Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention 80
2018 CVPR Multimodal Explanations: Justifying Decisions and Pointing to the Evidence 78
2018 arxiv Manipulating and measuring model interpretability 73
2018 ICML Learning to explain: An information-theoretic perspective on model interpretation 72
2017 arxiv Challenges for transparency 69
2017 arxiv Interpretable & explorable approximations of black box models 68
2018 AAAI Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions 67
2017 EMNLP A causal framework for explaining the predictions of black-box sequence-to-sequence models 64
2017 CEURW What does explainable AI really mean? A new conceptualization of perspectives 64
2019 AAAI Interpretation of neural networks is fragile 63
2019 ACL Attention is not Explanation 57
2018 TPAMI Interpreting deep visual representations via network dissection 56
2017 ACL Visualizing and Understanding Neural Machine Translation 56
2019 NMI Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead 54
2019 ACMFAT Explaining explanations in AI 54
2018 CVPR Transparency by design: Closing the gap between performance and interpretability in visual reasoning 54
2018 AAAI Interpreting CNN Knowledge via an Explanatory Graph 54
2018 MIPRO Explainable artificial intelligence: A survey 54
2019 CVPR Interpreting CNNs via Decision Trees 49
2017 survey Interpretability of deep learning models: a survey of results 49
2018 ICML Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples 47
2019 CVPR From Recognition to Cognition: Visual Commonsense Reasoning 44
2017 arxiv Towards interpretable deep neural networks by leveraging adversarial examples 44
2017 CVPR Improving Interpretability of Deep Neural Networks with Semantic Information 43
2016 arxiv Attentive Explanations: Justifying Decisions and Pointing to the Evidence 41
2018 ECCV Explainable neural computation via stack neural module networks 40
2018 CVPR Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks 39
2017 ICCV Understanding and comparing deep neural networks for age and gender classification 39
2018 ECCV Grounding visual explanations 38
2019 NIPS This looks like that: deep learning for interpretable image recognition 35
2018 NIPS Explanations based on the missing: Towards contrastive explanations with pertinent negatives 35
2017 IJCAI Understanding and improving convolutional neural networks via concatenated rectified linear units 35
2018 ACL Did the Model Understand the Question? 34
2018 ICLR Detecting statistical interactions from neural network weights 30
2018 ECCV Textual explanations for self-driving vehicles 30
2018 BMVC Rise: Randomized input sampling for explanation of black-box models 30
2017 arxiv Contextual Explanation Networks 28
2016 ICML Visualizing and comparing AlexNet and VGG using deconvolutional layers 28
2018 NIPS Towards robust interpretability with self-explaining neural networks 27
2018 AIES Detecting Bias in Black-Box Models Using Transparent Model Distillation 27
2018 arxiv How convolutional neural network see the world-A survey of convolutional neural network visualization methods 27
2018 ECCV Interpretable basis decomposition for visual explanation 26
2018 NIPS Attacks meet interpretability: Attribute-steered detection of adversarial samples 26
2017 ICLR Exploring LOTS in Deep Neural Networks 26
2017 AAAI Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning 26
2018 arxiv Revisiting the importance of individual units in cnns via ablation 25
2018 AAAI Examining CNN Representations with respect to Dataset Bias 24
2016 arxiv Investigating the influence of noise and distractors on the interpretation of neural networks 24
2016 IJCV Visualizing Object Detection Features 22
2018 ICLR Interpretable counting for visual question answering 21
2018 CVPR What have we learned from deep representations for action recognition? 20
2018 CVPR Learning to Act Properly: Predicting and Explaining Affordances from Images 17
2018 ECCV Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases 17
2018 NIPS Workshop Interpretable Convolutional Filters with SincNet 17
2019 JVCIR Interpretable convolutional neural networks via feedforward design 16
2019 ICLR Hierarchical interpretations for neural network predictions 15
2018 NIPS DeepPINK: reproducible feature selection in deep neural networks 15
2017 CVPR Mining Object Parts from CNNs via Active Question-Answering 15
2019 CVPR Attention branch network: Learning of attention mechanism for visual explanation 14
2017 CVPRW Looking under the hood: Deep neural network visualization to interpret whole-slide image analysis outcomes for colorectal polyps 14
2018 CVPR Teaching Categories to Human Learners with Visual Explanations 13
2018 ECCV Vqa-e: Explaining, elaborating, and enhancing your answers for visual questions 12
2018 NIPS Representer point selection for explaining deep neural networks 11
2016 ECCV Design of kernels in convolutional neural networks for image classification 11
2019 ICLR How Important Is a Neuron? 10
2017 ICCV Learning to disambiguate by asking discriminative questions 10
2019 AAAIW Unsupervised Learning of Neural Networks to Explain Neural Networks 9
2018 CVPR What do Deep Networks Like to See? 9
2019 CVPR Interpretable and fine-grained visual explanations for convolutional neural networks 8
2018 ECCV Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance 8
2019 ICLR Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks 7
2019 ICAIS Interpreting black box predictions using fisher kernels 7
2019 CVPR Learning to Explain with Complemental Examples 6
2019 ICCV U-CAM: Visual Explanation using Uncertainty based Class Activation Maps 6
2019 ICCV Towards Interpretable Face Recognition 6
2019 CVPR Revealing Scenes by Inverting Structure from Motion Reconstructions 5
2019 ICCV Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded 5
2018 CVPR Interpret Neural Networks by Identifying Critical Data Routing Paths 5
2018 ECCV Diverse feature visualizations reveal invariances in early layers of deep neural networks 5
2019 ICML Towards A Deep and Unified Understanding of Deep Neural Models in NLP 4
2019 AAAI Classifier-agnostic saliency map extraction 4
2019 AAAIW Network Transplanting 4
2019 arxiv Attention Interpretability Across NLP Tasks 4
2019 NIPS A benchmark for interpretability methods in deep neural networks(同arxiv:1806.10758) 3
2019 arxiv Interpretable CNNs 3
2019 NIPS Full-gradient representation for neural network visualization 2
2019 NIPS On the (In) fidelity and Sensitivity of Explanations 2
2019 ICCV Understanding Deep Networks via Extremal Perturbations and Smooth Masks 2
2019 NIPS Towards Automatic Concept-based Explanations 1
2019 NIPS CXPlain: Causal explanations for model interpretation under uncertainty 1
2019 CVPR Multimodal Explanations by Predicting Counterfactuality in Videos 1
2019 CVPR Visualizing the Resilience of Deep Convolutional Network Interpretations 1
2019 ICCV Explaining Neural Networks Semantically and Quantitatively 1
2018 arxiv Computationally Efficient Measures of Internal Neuron Importance 1
2020 ICLR Knowledge Isomorphism between Neural Networks 0
2020 ICLR Interpretable Complex-Valued Neural Networks for Privacy Protection 0
2019 AAAI Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval 0
2018 ECCV ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations 0