2020 
CVPR 
Explaining Knowledge Distillation by Quantifying the Knowledge 
3 

2020 
CVPR 
Highfrequency Component Helps Explain the Generalization of Convolutional Neural Networks 
16 

2020 
CVPRW 
ScoreCAM: ScoreWeighted Visual Explanations for Convolutional Neural Networks 
7 
Pytorch 
2020 
ICLR 
Knowledge consistency between neural networks and beyond 
3 

2020 
ICLR 
Interpretable ComplexValued 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 
Fullgradient representation for neural network visualization 
7 

2019 
NeurIPS 
On the (In) fidelity and Sensitivity of Explanations 
13 

2019 
NeurIPS 
Towards Automatic Conceptbased 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 finegrained 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 
UCAM: 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 
Classifieragnostic saliency map extraction 
8 

2019 
AAAI 
Can You Explain That? Lucid Explanations Help HumanAI 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 gradientbased 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 selfdriving 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 
Vqae: Explaining, elaborating, and enhancing your answers for visual questions 
20 

2018 
ECCV 
Choose Your Neuron: Incorporating Domain Knowledge through NeuronImportance 
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 informationtheoretic 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 selfexplaining neural networks 
145 
Pytorch 
2018 
NeurIPS 
Attacks meet interpretability: Attributesteered 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: Highprecision modelagnostic 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 casebased 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 
Gradcam++: Generalized gradientbased visual explanations for deep convolutional networks 
174 

2018 
IJCV 
Topdown 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 blackbox: 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 blackbox models 
85 

2018 
arxiv 
DistillandCompare: Auditing BlackBox Models Using Transparent Model Distillation 
30 

2018 
arxiv 
Manipulating and measuring model interpretability 
133 

2018 
arxiv 
How convolutional neural network see the worldA 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 Blackbox 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 QuestionAnswering 
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 
Gradcam: Visual explanations from deep networks via gradientbased localization 
2444 
Pytorch 
2017 
ICCV 
Interpretable Explanations of Black Boxes by Meaningful Perturbation 
419 
Pytorch 
2017 
ICCV 
Interpretable Learning for SelfDriving 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 MultiShot Learning 
37 
Matlab 
2017 
ACL 
Visualizing and Understanding Neural Machine Translation 
92 

2017 
EMNLP 
A causal framework for explaining the predictions of blackbox sequencetosequence models 
92 

2017 
CVPR Workshop 
Looking under the hood: Deep neural network visualization to interpret wholeslide 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 IndustryScale 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 preimages 
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 
GradCAM: 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 computergenerated 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 
