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awesome_deep_learning_interpretability

深度学习近年来关于模型解释性的相关论文。

按引用次数排序可见引用排序

159篇论文pdf(有2篇需要上scihub找)上传到腾讯微云。

不定期更新。

Year Publication Paper Citation code
2020 CVPR Explaining Knowledge Distillation by Quantifying the Knowledge 81
2020 CVPR High-frequency Component Helps Explain the Generalization of Convolutional Neural Networks 289
2020 CVPRW Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks 414 Pytorch
2020 ICLR Knowledge consistency between neural networks and beyond 28
2020 ICLR Interpretable Complex-Valued Neural Networks for Privacy Protection 23
2019 AI Explanation in artificial intelligence: Insights from the social sciences 3248
2019 NMI Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead 3505
2019 NeurIPS Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift 1052 -
2019 NeurIPS This looks like that: deep learning for interpretable image recognition 665 Pytorch
2019 NeurIPS A benchmark for interpretability methods in deep neural networks 413
2019 NeurIPS Full-gradient representation for neural network visualization 155
2019 NeurIPS On the (In) fidelity and Sensitivity of Explanations 226
2019 NeurIPS Towards Automatic Concept-based Explanations 342 Tensorflow
2019 NeurIPS CXPlain: Causal explanations for model interpretation under uncertainty 133
2019 CVPR Interpreting CNNs via Decision Trees 293
2019 CVPR From Recognition to Cognition: Visual Commonsense Reasoning 544 Pytorch
2019 CVPR Attention branch network: Learning of attention mechanism for visual explanation 371
2019 CVPR Interpretable and fine-grained visual explanations for convolutional neural networks 116
2019 CVPR Learning to Explain with Complemental Examples 36
2019 CVPR Revealing Scenes by Inverting Structure from Motion Reconstructions 84 Tensorflow
2019 CVPR Multimodal Explanations by Predicting Counterfactuality in Videos 26
2019 CVPR Visualizing the Resilience of Deep Convolutional Network Interpretations 2
2019 ICCV U-CAM: Visual Explanation using Uncertainty based Class Activation Maps 61
2019 ICCV Towards Interpretable Face Recognition 66
2019 ICCV Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded 163
2019 ICCV Understanding Deep Networks via Extremal Perturbations and Smooth Masks 276 Pytorch
2019 ICCV Explaining Neural Networks Semantically and Quantitatively 49
2019 ICLR Hierarchical interpretations for neural network predictions 111 Pytorch
2019 ICLR How Important Is a Neuron? 101
2019 ICLR Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks 56
2018 ICML Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples 169 Pytorch
2019 ICML Towards A Deep and Unified Understanding of Deep Neural Models in NLP 80 Pytorch
2019 ICAIS Interpreting black box predictions using fisher kernels 80
2019 ACMFAT Explaining explanations in AI 558
2019 AAAI Interpretation of neural networks is fragile 597 Tensorflow
2019 AAAI Classifier-agnostic saliency map extraction 23
2019 AAAI Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval 11
2019 AAAIW Unsupervised Learning of Neural Networks to Explain Neural Networks 28
2019 AAAIW Network Transplanting 4
2019 CSUR A Survey of Methods for Explaining Black Box Models 3088
2019 JVCIR Interpretable convolutional neural networks via feedforward design 134 Keras
2019 ExplainAI The (Un)reliability of saliency methods 515
2019 ACL Attention is not Explanation 920
2019 EMNLP Attention is not not Explanation 667
2019 arxiv Attention Interpretability Across NLP Tasks 129
2019 arxiv Interpretable CNNs 2
2018 ICLR Towards better understanding of gradient-based attribution methods for deep neural networks 775
2018 ICLR Learning how to explain neural networks: PatternNet and PatternAttribution 342
2018 ICLR On the importance of single directions for generalization 282 Pytorch
2018 ICLR Detecting statistical interactions from neural network weights 148 Pytorch
2018 ICLR Interpretable counting for visual question answering 55 Pytorch
2018 CVPR Interpretable Convolutional Neural Networks 677
2018 CVPR Tell me where to look: Guided attention inference network 454 Chainer
2018 CVPR Multimodal Explanations: Justifying Decisions and Pointing to the Evidence 349 Caffe
2018 CVPR Transparency by design: Closing the gap between performance and interpretability in visual reasoning 180 Pytorch
2018 CVPR Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks 186
2018 CVPR What have we learned from deep representations for action recognition? 52
2018 CVPR Learning to Act Properly: Predicting and Explaining Affordances from Images 57
2018 CVPR Teaching Categories to Human Learners with Visual Explanations 64 Pytorch
2018 CVPR What do deep networks like to see? 36
2018 CVPR Interpret Neural Networks by Identifying Critical Data Routing Paths 73 Tensorflow
2018 ECCV Deep clustering for unsupervised learning of visual features 2056 Pytorch
2018 ECCV Explainable neural computation via stack neural module networks 164 Tensorflow
2018 ECCV Grounding visual explanations 184
2018 ECCV Textual explanations for self-driving vehicles 196
2018 ECCV Interpretable basis decomposition for visual explanation 228 Pytorch
2018 ECCV Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases 147
2018 ECCV Vqa-e: Explaining, elaborating, and enhancing your answers for visual questions 71
2018 ECCV Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance 41 Pytorch
2018 ECCV Diverse feature visualizations reveal invariances in early layers of deep neural networks 23 Tensorflow
2018 ECCV ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations 36
2018 ICML Interpretability beyond feature attribution: Quantitative testing with concept activation vectors 1130 Tensorflow
2018 ICML Learning to explain: An information-theoretic perspective on model interpretation 421
2018 ACL Did the Model Understand the Question? 171 Tensorflow
2018 FITEE Visual interpretability for deep learning: a survey 731
2018 NeurIPS Sanity Checks for Saliency Maps 1353
2018 NeurIPS Explanations based on the missing: Towards contrastive explanations with pertinent negatives 443 Tensorflow
2018 NeurIPS Towards robust interpretability with self-explaining neural networks 648 Pytorch
2018 NeurIPS Attacks meet interpretability: Attribute-steered detection of adversarial samples 142
2018 NeurIPS DeepPINK: reproducible feature selection in deep neural networks 125 Keras
2018 NeurIPS Representer point selection for explaining deep neural networks 182 Tensorflow
2018 NeurIPS Workshop Interpretable convolutional filters with sincNet 97
2018 AAAI Anchors: High-precision model-agnostic explanations 1517
2018 AAAI Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients 537 Tensorflow
2018 AAAI Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions 396 Tensorflow
2018 AAAI Interpreting CNN Knowledge via an Explanatory Graph 199 Matlab
2018 AAAI Examining CNN Representations with respect to Dataset Bias 88
2018 WACV Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks 1459
2018 IJCV Top-down neural attention by excitation backprop 778
2018 TPAMI Interpreting deep visual representations via network dissection 252
2018 DSP Methods for interpreting and understanding deep neural networks 2046
2018 Access Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI) 3110
2018 JAIR Learning Explanatory Rules from Noisy Data 440 Tensorflow
2018 MIPRO Explainable artificial intelligence: A survey 794
2018 BMVC Rise: Randomized input sampling for explanation of black-box models 657
2018 arxiv Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation 194
2018 arxiv Manipulating and measuring model interpretability 496
2018 arxiv How convolutional neural network see the world-A survey of convolutional neural network visualization methods 211
2018 arxiv Revisiting the importance of individual units in cnns via ablation 93
2018 arxiv Computationally Efficient Measures of Internal Neuron Importance 10
2017 ICML Understanding Black-box Predictions via Influence Functions 2062 Pytorch
2017 ICML Axiomatic attribution for deep networks 3654 Keras
2017 ICML Learning Important Features Through Propagating Activation Differences 2835
2017 ICLR Visualizing deep neural network decisions: Prediction difference analysis 674 Caffe
2017 ICLR Exploring LOTS in Deep Neural Networks 34
2017 NeurIPS A Unified Approach to Interpreting Model Predictions 11511
2017 NeurIPS Real time image saliency for black box classifiers 483 Pytorch
2017 NeurIPS SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability 473
2017 CVPR Mining Object Parts from CNNs via Active Question-Answering 29
2017 CVPR Network dissection: Quantifying interpretability of deep visual representations 1254
2017 CVPR Improving Interpretability of Deep Neural Networks with Semantic Information 118
2017 CVPR MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network 307 Torch
2017 CVPR Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering 1686
2017 CVPR Knowing when to look: Adaptive attention via a visual sentinel for image captioning 1392 Torch
2017 CVPRW Interpretable 3d human action analysis with temporal convolutional networks 539
2017 ICCV Grad-cam: Visual explanations from deep networks via gradient-based localization 13006 Pytorch
2017 ICCV Interpretable Explanations of Black Boxes by Meaningful Perturbation 1293 Pytorch
2017 ICCV Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention 323
2017 ICCV Understanding and comparing deep neural networks for age and gender classification 130
2017 ICCV Learning to disambiguate by asking discriminative questions 26
2017 IJCAI Right for the right reasons: Training differentiable models by constraining their explanations 429
2017 IJCAI Understanding and improving convolutional neural networks via concatenated rectified linear units 510 Caffe
2017 AAAI Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning 67 Matlab
2017 ACL Visualizing and Understanding Neural Machine Translation 179
2017 EMNLP A causal framework for explaining the predictions of black-box sequence-to-sequence models 192
2017 CVPR Workshop Looking under the hood: Deep neural network visualization to interpret whole-slide image analysis outcomes for colorectal polyps 47
2017 survey Interpretability of deep learning models: a survey of results 345
2017 arxiv SmoothGrad: removing noise by adding noise 1479
2017 arxiv Interpretable & explorable approximations of black box models 259
2017 arxiv Distilling a neural network into a soft decision tree 520 Pytorch
2017 arxiv Towards interpretable deep neural networks by leveraging adversarial examples 111
2017 arxiv Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models 1279
2017 arxiv Contextual Explanation Networks 77 Pytorch
2017 arxiv Challenges for transparency 142
2017 ACMSOPP Deepxplore: Automated whitebox testing of deep learning systems 1144
2017 CEURW What does explainable AI really mean? A new conceptualization of perspectives 518
2017 TVCG ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models 346
2016 NeurIPS Synthesizing the preferred inputs for neurons in neural networks via deep generator networks 659 Caffe
2016 NeurIPS Understanding the effective receptive field in deep convolutional neural networks 1356
2016 CVPR Inverting Visual Representations with Convolutional Networks 626
2016 CVPR Visualizing and Understanding Deep Texture Representations 147
2016 CVPR Analyzing Classifiers: Fisher Vectors and Deep Neural Networks 191
2016 ECCV Generating Visual Explanations 613 Caffe
2016 ECCV Design of kernels in convolutional neural networks for image classification 24
2016 ICML Understanding and improving convolutional neural networks via concatenated rectified linear units 510
2016 ICML Visualizing and comparing AlexNet and VGG using deconvolutional layers 126
2016 EMNLP Rationalizing Neural Predictions 738 Pytorch
2016 IJCV Visualizing deep convolutional neural networks using natural pre-images 508 Matlab
2016 IJCV Visualizing Object Detection Features 38 Caffe
2016 KDD Why should i trust you?: Explaining the predictions of any classifier 11742
2016 TVCG Visualizing the hidden activity of artificial neural networks 309
2016 TVCG Towards better analysis of deep convolutional neural networks 474
2016 NAACL Visualizing and understanding neural models in nlp 650 Torch
2016 arxiv Understanding neural networks through representation erasure) 492
2016 arxiv Grad-CAM: Why did you say that? 398
2016 arxiv Investigating the influence of noise and distractors on the interpretation of neural networks 108
2016 arxiv Attentive Explanations: Justifying Decisions and Pointing to the Evidence 88
2016 arxiv The Mythos of Model Interpretability 3786
2016 arxiv Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks 317
2015 ICLR Striving for Simplicity: The All Convolutional Net 4645 Pytorch
2015 CVPR Understanding deep image representations by inverting them 1942 Matlab
2015 ICCV Understanding deep features with computer-generated imagery 156 Caffe
2015 ICML Workshop Understanding Neural Networks Through Deep Visualization 2038 Tensorflow
2015 AAS Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model 749
2014 ECCV Visualizing and Understanding Convolutional Networks 18604 Pytorch
2014 ICLR Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps 6142 Pytorch
2013 ICCV Hoggles: Visualizing object detection features 352
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深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)

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