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awesome_deep_learning_interpretability

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所属分类神经网络/人工智能、机器学习/深度学习

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投递者丁震博

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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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