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All Projects → oneTaken → Awesome_deep_learning_interpretability

oneTaken / Awesome_deep_learning_interpretability

Licence: mit
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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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 3
2020 CVPR High-frequency Component Helps Explain the Generalization of Convolutional Neural Networks 16
2020 CVPRW Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks 7 Pytorch
2020 ICLR Knowledge consistency between neural networks and beyond 3
2020 ICLR Interpretable Complex-Valued Neural Networks for Privacy Protection 2
2019 AI Explanation in artificial intelligence: Insights from the social sciences 662
2019 NMI Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead 389
2019 NeurIPS Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift 136 -
2019 NeurIPS This looks like that: deep learning for interpretable image recognition 80 Pytorch
2019 NeurIPS A benchmark for interpretability methods in deep neural networks 28
2019 NeurIPS Full-gradient representation for neural network visualization 7
2019 NeurIPS On the (In) fidelity and Sensitivity of Explanations 13
2019 NeurIPS Towards Automatic Concept-based Explanations 25 Tensorflow
2019 NeurIPS CXPlain: Causal explanations for model interpretation under uncertainty 12
2019 CVPR Interpreting CNNs via Decision Trees 85
2019 CVPR From Recognition to Cognition: Visual Commonsense Reasoning 97 Pytorch
2019 CVPR Attention branch network: Learning of attention mechanism for visual explanation 39
2019 CVPR Interpretable and fine-grained visual explanations for convolutional neural networks 18
2019 CVPR Learning to Explain with Complemental Examples 12
2019 CVPR Revealing Scenes by Inverting Structure from Motion Reconstructions 20 Tensorflow
2019 CVPR Multimodal Explanations by Predicting Counterfactuality in Videos 4
2019 CVPR Visualizing the Resilience of Deep Convolutional Network Interpretations 1
2019 ICCV U-CAM: Visual Explanation using Uncertainty based Class Activation Maps 10
2019 ICCV Towards Interpretable Face Recognition 7
2019 ICCV Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded 28
2019 ICCV Understanding Deep Networks via Extremal Perturbations and Smooth Masks 17 Pytorch
2019 ICCV Explaining Neural Networks Semantically and Quantitatively 6
2019 ICLR Hierarchical interpretations for neural network predictions 24 Pytorch
2019 ICLR How Important Is a Neuron? 32
2019 ICLR Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks 13
2018 ICML Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples 71 Pytorch
2019 ICML Towards A Deep and Unified Understanding of Deep Neural Models in NLP 15 Pytorch
2019 ICAIS Interpreting black box predictions using fisher kernels 24
2019 ACMFAT Explaining explanations in AI 119
2019 AAAI Interpretation of neural networks is fragile 130 Tensorflow
2019 AAAI Classifier-agnostic saliency map extraction 8
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 655
2019 JVCIR Interpretable convolutional neural networks via feedforward design 31 Keras
2019 ExplainAI The (Un)reliability of saliency methods 128
2019 ACL Attention is not Explanation 157
2019 EMNLP Attention is not not Explanation 57
2019 arxiv Attention Interpretability Across NLP Tasks 16
2019 arxiv Interpretable CNNs 2
2018 ICLR Towards better understanding of gradient-based attribution methods for deep neural networks 245
2018 ICLR Learning how to explain neural networks: PatternNet and PatternAttribution 143
2018 ICLR On the importance of single directions for generalization 134 Pytorch
2018 ICLR Detecting statistical interactions from neural network weights 56 Pytorch
2018 ICLR Interpretable counting for visual question answering 29 Pytorch
2018 CVPR Interpretable Convolutional Neural Networks 250
2018 CVPR Tell me where to look: Guided attention inference network 134 Chainer
2018 CVPR Multimodal Explanations: Justifying Decisions and Pointing to the Evidence 126 Caffe
2018 CVPR Transparency by design: Closing the gap between performance and interpretability in visual reasoning 79 Pytorch
2018 CVPR Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks 60
2018 CVPR What have we learned from deep representations for action recognition? 30
2018 CVPR Learning to Act Properly: Predicting and Explaining Affordances from Images 24
2018 CVPR Teaching Categories to Human Learners with Visual Explanations 20 Pytorch
2018 CVPR What do deep networks like to see? 19
2018 CVPR Interpret Neural Networks by Identifying Critical Data Routing Paths 13 Tensorflow
2018 ECCV Deep clustering for unsupervised learning of visual features 382 Pytorch
2018 ECCV Explainable neural computation via stack neural module networks 55 Tensorflow
2018 ECCV Grounding visual explanations 44
2018 ECCV Textual explanations for self-driving vehicles 59
2018 ECCV Interpretable basis decomposition for visual explanation 51 Pytorch
2018 ECCV Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases 36
2018 ECCV Vqa-e: Explaining, elaborating, and enhancing your answers for visual questions 20
2018 ECCV Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance 16 Pytorch
2018 ECCV Diverse feature visualizations reveal invariances in early layers of deep neural networks 9 Tensorflow
2018 ECCV ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations 6
2018 ICML Interpretability beyond feature attribution: Quantitative testing with concept activation vectors 214 Tensorflow
2018 ICML Learning to explain: An information-theoretic perspective on model interpretation 117
2018 ACL Did the Model Understand the Question? 63 Tensorflow
2018 FITEE Visual interpretability for deep learning: a survey 243
2018 NeurIPS Sanity Checks for Saliency Maps 249
2018 NeurIPS Explanations based on the missing: Towards contrastive explanations with pertinent negatives 79 Tensorflow
2018 NeurIPS Towards robust interpretability with self-explaining neural networks 145 Pytorch
2018 NeurIPS Attacks meet interpretability: Attribute-steered detection of adversarial samples 55
2018 NeurIPS DeepPINK: reproducible feature selection in deep neural networks 30 Keras
2018 NeurIPS Representer point selection for explaining deep neural networks 30 Tensorflow
2018 NeurIPS Workshop Interpretable convolutional filters with sincNet 37
2018 AAAI Anchors: High-precision model-agnostic explanations 366
2018 AAAI Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients 178 Tensorflow
2018 AAAI Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions 102 Tensorflow
2018 AAAI Interpreting CNN Knowledge via an Explanatory Graph 79 Matlab
2018 AAAI Examining CNN Representations with respect to Dataset Bias 37
2018 WACV Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks 174
2018 IJCV Top-down neural attention by excitation backprop 329
2018 TPAMI Interpreting deep visual representations via network dissection 87
2018 DSP Methods for interpreting and understanding deep neural networks 713
2018 Access Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI) 390
2018 JAIR Learning Explanatory Rules from Noisy Data 155 Tensorflow
2018 MIPRO Explainable artificial intelligence: A survey 108
2018 BMVC Rise: Randomized input sampling for explanation of black-box models 85
2018 arxiv Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation 30
2018 arxiv Manipulating and measuring model interpretability 133
2018 arxiv How convolutional neural network see the world-A survey of convolutional neural network visualization methods 45
2018 arxiv Revisiting the importance of individual units in cnns via ablation 43
2018 arxiv Computationally Efficient Measures of Internal Neuron Importance 1
2017 ICML Understanding Black-box Predictions via Influence Functions 767 Pytorch
2017 ICML Axiomatic attribution for deep networks 755 Keras
2017 ICML Learning Important Features Through Propagating Activation Differences 655
2017 ICLR Visualizing deep neural network decisions: Prediction difference analysis 271 Caffe
2017 ICLR Exploring LOTS in Deep Neural Networks 27
2017 NeurIPS A Unified Approach to Interpreting Model Predictions 1411
2017 NeurIPS Real time image saliency for black box classifiers 161 Pytorch
2017 NeurIPS SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability 160
2017 CVPR Mining Object Parts from CNNs via Active Question-Answering 20
2017 CVPR Network dissection: Quantifying interpretability of deep visual representations 540
2017 CVPR Improving Interpretability of Deep Neural Networks with Semantic Information 56
2017 CVPR MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network 129 Torch
2017 CVPR Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering 582
2017 CVPR Knowing when to look: Adaptive attention via a visual sentinel for image captioning 620 Torch
2017 CVPRW Interpretable 3d human action analysis with temporal convolutional networks 163
2017 ICCV Grad-cam: Visual explanations from deep networks via gradient-based localization 2444 Pytorch
2017 ICCV Interpretable Explanations of Black Boxes by Meaningful Perturbation 419 Pytorch
2017 ICCV Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention 114
2017 ICCV Understanding and comparing deep neural networks for age and gender classification 52
2017 ICCV Learning to disambiguate by asking discriminative questions 12
2017 IJCAI Right for the right reasons: Training differentiable models by constraining their explanations 149
2017 IJCAI Understanding and improving convolutional neural networks via concatenated rectified linear units 276 Caffe
2017 AAAI Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning 37 Matlab
2017 ACL Visualizing and Understanding Neural Machine Translation 92
2017 EMNLP A causal framework for explaining the predictions of black-box sequence-to-sequence models 92
2017 CVPR Workshop Looking under the hood: Deep neural network visualization to interpret whole-slide image analysis outcomes for colorectal polyps 21
2017 survey Interpretability of deep learning models: a survey of results 99
2017 arxiv SmoothGrad: removing noise by adding noise 356
2017 arxiv Interpretable & explorable approximations of black box models 115
2017 arxiv Distilling a neural network into a soft decision tree 188 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 383
2017 arxiv Contextual Explanation Networks 35 Pytorch
2017 arxiv Challenges for transparency 83
2017 ACMSOPP Deepxplore: Automated whitebox testing of deep learning systems 431
2017 CEURW What does explainable AI really mean? A new conceptualization of perspectives 117
2017 TVCG ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models 158
2016 NeurIPS Synthesizing the preferred inputs for neurons in neural networks via deep generator networks 321 Caffe
2016 NeurIPS Understanding the effective receptive field in deep convolutional neural networks 436
2016 CVPR Inverting Visual Representations with Convolutional Networks 336
2016 CVPR Visualizing and Understanding Deep Texture Representations 98
2016 CVPR Analyzing Classifiers: Fisher Vectors and Deep Neural Networks 110
2016 ECCV Generating Visual Explanations 303 Caffe
2016 ECCV Design of kernels in convolutional neural networks for image classification 14
2016 ICML Understanding and improving convolutional neural networks via concatenated rectified linear units 276
2016 ICML Visualizing and comparing AlexNet and VGG using deconvolutional layers 41
2016 EMNLP Rationalizing Neural Predictions 355 Pytorch
2016 IJCV Visualizing deep convolutional neural networks using natural pre-images 281 Matlab
2016 IJCV Visualizing Object Detection Features 27 Caffe
2016 KDD Why should i trust you?: Explaining the predictions of any classifier 3511
2016 TVCG Visualizing the hidden activity of artificial neural networks 170
2016 TVCG Towards better analysis of deep convolutional neural networks 241
2016 NAACL Visualizing and understanding neural models in nlp 364 Torch
2016 arxiv Understanding neural networks through representation erasure) 198
2016 arxiv Grad-CAM: Why did you say that? 130
2016 arxiv Investigating the influence of noise and distractors on the interpretation of neural networks 41
2016 arxiv Attentive Explanations: Justifying Decisions and Pointing to the Evidence 54
2016 arxiv The Mythos of Model Interpretability 1368
2016 arxiv Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks 161
2015 ICLR Striving for Simplicity: The All Convolutional Net 2268 Pytorch
2015 CVPR Understanding deep image representations by inverting them 1129 Matlab
2015 ICCV Understanding deep features with computer-generated imagery 109 Caffe
2015 ICML Workshop Understanding Neural Networks Through Deep Visualization 1216 Tensorflow
2015 AAS Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model 385
2014 ECCV Visualizing and Understanding Convolutional Networks 9873 Pytorch
2014 ICLR Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps 2745 Pytorch
2013 ICCV Hoggles: Visualizing object detection features 301
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