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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 0
2020 ICLR Knowledge Isomorphism between Neural Networks 0
2020 ICLR Interpretable Complex-Valued Neural Networks for Privacy Protection 2
2019 AI Explanation in artificial intelligence: Insights from the social sciences 552
2019 NMI Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead 192
2019 NeurIPS This looks like that: deep learning for interpretable image recognition 55 Pytorch
2019 NeurIPS A benchmark for interpretability methods in deep neural networks 14
2019 NeurIPS Full-gradient representation for neural network visualization 3
2019 NeurIPS On the (In) fidelity and Sensitivity of Explanations 4
2019 NeurIPS Towards Automatic Concept-based Explanations 9 Tensorflow
2019 NeurIPS CXPlain: Causal explanations for model interpretation under uncertainty 4
2019 CVPR Interpreting CNNs via Decision Trees 71
2019 CVPR From Recognition to Cognition: Visual Commonsense Reasoning 74 Pytorch
2019 CVPR Attention branch network: Learning of attention mechanism for visual explanation 28
2019 CVPR Interpretable and fine-grained visual explanations for convolutional neural networks 10
2019 CVPR Learning to Explain with Complemental Examples 10
2019 CVPR Revealing Scenes by Inverting Structure from Motion Reconstructions 12 Tensorflow
2019 CVPR Multimodal Explanations by Predicting Counterfactuality in Videos 3
2019 CVPR Visualizing the Resilience of Deep Convolutional Network Interpretations 1
2019 ICCV U-CAM: Visual Explanation using Uncertainty based Class Activation Maps 9
2019 ICCV Towards Interpretable Face Recognition 7
2019 ICCV Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded 14
2019 ICCV Understanding Deep Networks via Extremal Perturbations and Smooth Masks 9 Pytorch
2019 ICCV Explaining Neural Networks Semantically and Quantitatively 5
2019 ICLR Hierarchical interpretations for neural network predictions 24 Pytorch
2019 ICLR How Important Is a Neuron? 10
2019 ICLR Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks 10
2018 ICML Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples 64 Pytorch
2019 ICML Towards A Deep and Unified Understanding of Deep Neural Models in NLP 10 Pytorch
2019 ICAIS Interpreting black box predictions using fisher kernels 16
2019 ACMFAT Explaining explanations in AI 92
2019 AAAI Interpretation of neural networks is fragile 100 Tensorflow
2019 AAAI Classifier-agnostic saliency map extraction 7
2019 AAAI Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval 1
2019 AAAIW Unsupervised Learning of Neural Networks to Explain Neural Networks 10
2019 AAAIW Network Transplanting 4
2019 CSUR A Survey of Methods for Explaining Black Box Models 518
2019 JVCIR Interpretable convolutional neural networks via feedforward design 28 Keras
2019 ExplainAI The (Un)reliability of saliency methods 128
2019 ACL Attention is not Explanation 88
2019 EMNLP Attention is not not Explanation 22
2019 arxiv Attention Interpretability Across NLP Tasks 6
2019 arxiv Interpretable CNNs 2
2018 ICLR Towards better understanding of gradient-based attribution methods for deep neural networks 160
2018 ICLR Learning how to explain neural networks: PatternNet and PatternAttribution 113
2018 ICLR On the importance of single directions for generalization 109 Pytorch
2018 ICLR Detecting statistical interactions from neural network weights 45 Pytorch
2018 ICLR Interpretable counting for visual question answering 27 Pytorch
2018 CVPR Interpretable Convolutional Neural Networks 213
2018 CVPR Tell me where to look: Guided attention inference network 110 Chainer
2018 CVPR Multimodal Explanations: Justifying Decisions and Pointing to the Evidence 106 Caffe
2018 CVPR Transparency by design: Closing the gap between performance and interpretability in visual reasoning 70 Pytorch
2018 CVPR Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks 48
2018 CVPR What have we learned from deep representations for action recognition? 25
2018 CVPR Learning to Act Properly: Predicting and Explaining Affordances from Images 23
2018 CVPR Teaching Categories to Human Learners with Visual Explanations 19 Pytorch
2018 CVPR What do deep networks like to see? 17
2018 CVPR Interpret Neural Networks by Identifying Critical Data Routing Paths 10 Tensorflow
2018 ECCV Deep clustering for unsupervised learning of visual features 289 Pytorch
2018 ECCV Explainable neural computation via stack neural module networks 43 Tensorflow
2018 ECCV Grounding visual explanations 40
2018 ECCV Textual explanations for self-driving vehicles 47
2018 ECCV Interpretable basis decomposition for visual explanation 40 Pytorch
2018 ECCV Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases 23
2018 ECCV Vqa-e: Explaining, elaborating, and enhancing your answers for visual questions 17
2018 ECCV Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance 14 Pytorch
2018 ECCV Diverse feature visualizations reveal invariances in early layers of deep neural networks 8 Tensorflow
2018 ECCV ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations 2
2018 ICML Interpretability beyond feature attribution: Quantitative testing with concept activation vectors 168 Tensorflow
2018 ICML Learning to explain: An information-theoretic perspective on model interpretation 94
2018 ACL Did the Model Understand the Question? 42 Tensorflow
2018 FITEE Visual interpretability for deep learning: a survey 208
2018 NeurIPS Sanity Checks for Saliency Maps 195
2018 NeurIPS Explanations based on the missing: Towards contrastive explanations with pertinent negatives 53 Tensorflow
2018 NeurIPS Towards robust interpretability with self-explaining neural networks 101 Pytorch
2018 NeurIPS Attacks meet interpretability: Attribute-steered detection of adversarial samples 42
2018 NeurIPS DeepPINK: reproducible feature selection in deep neural networks 22 Keras
2018 NeurIPS Representer point selection for explaining deep neural networks 20 Tensorflow
2018 NeurIPS Workshop Interpretable convolutional filters with sincNet 30
2018 AAAI Anchors: High-precision model-agnostic explanations 290
2018 AAAI Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients 146 Tensorflow
2018 AAAI Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions 87 Tensorflow
2018 AAAI Interpreting CNN Knowledge via an Explanatory Graph 70 Matlab
2018 AAAI Examining CNN Representations with respect to Dataset Bias 33
2018 WACV Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks 127
2018 IJCV Top-down neural attention by excitation backprop 297
2018 TPAMI Interpreting deep visual representations via network dissection 73
2018 DSP Methods for interpreting and understanding deep neural networks 617
2018 Access Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI) 279
2018 JAIR Learning Explanatory Rules from Noisy Data 127 Tensorflow
2018 MIPRO Explainable artificial intelligence: A survey 92
2018 BMVC Rise: Randomized input sampling for explanation of black-box models 62
2018 arxiv Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation 25
2018 arxiv Manipulating and measuring model interpretability 106
2018 arxiv How convolutional neural network see the world-A survey of convolutional neural network visualization methods 36
2018 arxiv Revisiting the importance of individual units in cnns via ablation 33
2018 arxiv Computationally Efficient Measures of Internal Neuron Importance 1
2017 ICML Understanding Black-box Predictions via Influence Functions 641 Pytorch
2017 ICML Axiomatic attribution for deep networks 623 Keras
2017 ICML Learning Important Features Through Propagating Activation Differences 531
2017 ICLR Visualizing deep neural network decisions: Prediction difference analysis 241 Caffe
2017 ICLR Exploring LOTS in Deep Neural Networks 26
2017 NeurIPS A Unified Approach to Interpreting Model Predictions 1036
2017 NeurIPS Real time image saliency for black box classifiers 141 Pytorch
2017 NeurIPS SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability 131
2017 CVPR Mining Object Parts from CNNs via Active Question-Answering 17
2017 CVPR Network dissection: Quantifying interpretability of deep visual representations 463
2017 CVPR Improving Interpretability of Deep Neural Networks with Semantic Information 52
2017 CVPR MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network 109 Torch
2017 CVPR Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering 509
2017 CVPR Knowing when to look: Adaptive attention via a visual sentinel for image captioning 553 Torch
2017 CVPRW Interpretable 3d human action analysis with temporal convolutional networks 140
2017 ICCV Grad-cam: Visual explanations from deep networks via gradient-based localization 1980 Pytorch
2017 ICCV Interpretable Explanations of Black Boxes by Meaningful Perturbation 371 Pytorch
2017 ICCV Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention 101
2017 ICCV Understanding and comparing deep neural networks for age and gender classification 52
2017 ICCV Learning to disambiguate by asking discriminative questions 11
2017 IJCAI Right for the right reasons: Training differentiable models by constraining their explanations 125
2017 IJCAI Understanding and improving convolutional neural networks via concatenated rectified linear units 250 Caffe
2017 AAAI Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning 31 Matlab
2017 ACL Visualizing and Understanding Neural Machine Translation 69
2017 EMNLP A causal framework for explaining the predictions of black-box sequence-to-sequence models 74
2017 CVPR Workshop Looking under the hood: Deep neural network visualization to interpret whole-slide image analysis outcomes for colorectal polyps 19
2017 survey Interpretability of deep learning models: a survey of results 70
2017 arxiv SmoothGrad: removing noise by adding noise 289
2017 arxiv Interpretable & explorable approximations of black box models 97
2017 arxiv Distilling a neural network into a soft decision tree 162 Pytorch
2017 arxiv Towards interpretable deep neural networks by leveraging adversarial examples 54
2017 arxiv Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models 319
2017 arxiv Contextual Explanation Networks 32 Pytorch
2017 arxiv Challenges for transparency 75
2017 ACMSOPP Deepxplore: Automated whitebox testing of deep learning systems 384
2017 CEURW What does explainable AI really mean? A new conceptualization of perspectives 94
2017 TVCG ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models 141
2016 NeurIPS Synthesizing the preferred inputs for neurons in neural networks via deep generator networks 286 Caffe
2016 NeurIPS Understanding the effective receptive field in deep convolutional neural networks 391
2016 CVPR Inverting Visual Representations with Convolutional Networks 306
2016 CVPR Visualizing and Understanding Deep Texture Representations 90
2016 CVPR Analyzing Classifiers: Fisher Vectors and Deep Neural Networks 100
2016 ECCV Generating Visual Explanations 273 Caffe
2016 ECCV Design of kernels in convolutional neural networks for image classification 13
2016 ICML Understanding and improving convolutional neural networks via concatenated rectified linear units 250
2016 ICML Visualizing and comparing AlexNet and VGG using deconvolutional layers 35
2016 EMNLP Rationalizing Neural Predictions 288 Pytorch
2016 IJCV Visualizing deep convolutional neural networks using natural pre-images 254 Matlab
2016 IJCV Visualizing Object Detection Features 25 Caffe
2016 KDD Why should i trust you?: Explaining the predictions of any classifier 2984
2016 TVCG Visualizing the hidden activity of artificial neural networks 152
2016 TVCG Towards better analysis of deep convolutional neural networks 225
2016 NAACL Visualizing and understanding neural models in nlp 306 Torch
2016 arxiv Understanding neural networks through representation erasure) 155
2016 arxiv Grad-CAM: Why did you say that? 119
2016 arxiv Investigating the influence of noise and distractors on the interpretation of neural networks 31
2016 arxiv Attentive Explanations: Justifying Decisions and Pointing to the Evidence 47
2016 arxiv The Mythos of Model Interpretability 1177
2016 arxiv Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks 144
2015 ICLR Striving for Simplicity: The All Convolutional Net 2080 Pytorch
2015 CVPR Understanding deep image representations by inverting them 1046 Matlab
2015 ICCV Understanding deep features with computer-generated imagery 103 Caffe
2015 ICML Workshop Understanding Neural Networks Through Deep Visualization 1114 Tensorflow
2015 AAS Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model 331
2014 ECCV Visualizing and Understanding Convolutional Networks 9168 Pytorch
2014 ICLR Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps 2399 Pytorch
2013 ICCV Hoggles: Visualizing object detection features 293
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