| 2014 |
ECCV |
Visualizing and Understanding Convolutional Networks |
8009 |
| 2016 |
KDD |
Why should i trust you?: Explaining the predictions of any classifier |
2255 |
| 2014 |
ICLR |
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps |
2014 |
| 2015 |
ICLR |
Striving for Simplicity: The All Convolutional Net |
1762 |
| 2017 |
ICCV |
Grad-cam: Visual explanations from deep networks via gradient-based localization |
1333 |
| 2015 |
ICMLW |
Understanding Neural Networks Through Deep Visualization |
974 |
| 2016 |
arxiv |
The Mythos of Model Interpretability |
951 |
| 2015 |
CVPR |
Understanding deep image representations by inverting them |
929 |
| 2017 |
NIPS |
A Unified Approach to Interpreting Model Predictions |
591 |
| 2017 |
ICML |
Understanding Black-box Predictions via Influence Functions |
517 |
| 2018 |
DSP |
Methods for interpreting and understanding deep neural networks(scihub) |
469 |
| 2017 |
CVPR |
Knowing when to look: Adaptive attention via a visual sentinel for image captioning |
458 |
| 2017 |
ICML |
Axiomatic attribution for deep networks |
448 |
| 2017 |
CVPR |
Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering |
393 |
| 2017 |
ICML |
Learning Important Features Through Propagating Activation Differences |
383 |
| 2019 |
AI |
Explanation in artificial intelligence: Insights from the social sciences |
380 |
| 2017 |
CVPR |
Network dissection: Quantifying interpretability of deep visual representations |
373 |
| 2019 |
CSUR |
A Survey of Methods for Explaining Black Box Models |
344 |
| 2016 |
NIPS |
Understanding the effective receptive field in deep convolutional neural networks |
310 |
| 2015 |
AAS |
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model |
304 |
| 2017 |
ACMSOPP |
Deepxplore: Automated whitebox testing of deep learning systems |
302 |
| 2017 |
ICCV |
Interpretable Explanations of Black Boxes by Meaningful Perturbation |
284 |
| 2016 |
NAACL |
Visualizing and understanding neural models in nlp |
269 |
| 2016 |
CVPR |
Inverting Visual Representations with Convolutional Networks |
266 |
| 2018 |
IJCV |
Top-down neural attention by excitation backprop |
256 |
| 2016 |
NIPS |
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks |
251 |
| 2016 |
EMNLP |
Rationalizing Neural Predictions |
247 |
| 2016 |
ECCV |
Generating Visual Explanations |
224 |
| 2016 |
ICML |
Understanding and improving convolutional neural networks via concatenated rectified linear units |
216 |
| 2016 |
IJCV |
Visualizing deep convolutional neural networks using natural pre-images |
216 |
| 2017 |
ICLR |
Visualizing deep neural network decisions: Prediction difference analysis |
212 |
| 2017 |
arxiv |
SmoothGrad: removing noise by adding noise |
212 |
| 2017 |
arxiv |
Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models |
210 |
| 2018 |
AAAI |
Anchors: High-precision model-agnostic explanations |
200 |
| 2016 |
TVCG |
Towards better analysis of deep convolutional neural networks |
184 |
| 2018 |
ECCV |
Deep clustering for unsupervised learning of visual features |
167 |
| 2018 |
CVPR |
Interpretable Convolutional Neural Networks |
154 |
| 2018 |
FITEE |
Visual interpretability for deep learning: a survey |
140 |
| 2016 |
arxiv |
Understanding neural networks through representation erasure |
137 |
| 2018 |
Access |
Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI) |
131 |
| 2016 |
arxiv |
Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks |
130 |
| 2017 |
arxiv |
Distilling a neural network into a soft decision tree |
126 |
| 2018 |
ICLR |
Towards better understanding of gradient-based attribution methods for deep neural networks |
123 |
| 2018 |
NIPS |
Sanity Checks for Saliency Maps |
122 |
| 2016 |
TVCG |
Visualizing the hidden activity of artificial neural networks |
122 |
| 2017 |
TVCG |
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models |
113 |
| 2018 |
AAAI |
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients |
112 |
| 2017 |
NIPS |
Real time image saliency for black box classifiers |
111 |
| 2018 |
ICML |
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav) |
110 |
| 2017 |
CVPR |
Interpretable 3d human action analysis with temporal convolutional networks |
106 |
| 2017 |
IJCAI |
Right for the right reasons: Training differentiable models by constraining their explanations |
102 |
| 2017 |
NIPS |
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability |
97 |
| 2019 |
ExplainAI |
The (Un)reliability of saliency methods(scihub) |
95 |
| 2015 |
ICCV |
Understanding deep features with computer-generated imagery |
94 |
| 2018 |
ICLR |
Learning how to explain neural networks: PatternNet and PatternAttribution |
90 |
| 2018 |
JAIR |
Learning Explanatory Rules from Noisy Data |
90 |
| 2016 |
arxiv |
Grad-CAM: Why did you say that? |
87 |
| 2017 |
CVPR |
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network |
86 |
| 2018 |
WACV |
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks |
85 |
| 2016 |
CVPR |
Visualizing and Understanding Deep Texture Representations |
83 |
| 2016 |
CVPR |
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks |
82 |
| 2018 |
ICLR |
On the importance of single directions for generalization |
81 |
| 2018 |
CVPR |
Tell me where to look: Guided attention inference network |
81 |
| 2017 |
ICCV |
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention |
80 |
| 2018 |
CVPR |
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence |
78 |
| 2018 |
arxiv |
Manipulating and measuring model interpretability |
73 |
| 2018 |
ICML |
Learning to explain: An information-theoretic perspective on model interpretation |
72 |
| 2017 |
arxiv |
Challenges for transparency |
69 |
| 2017 |
arxiv |
Interpretable & explorable approximations of black box models |
68 |
| 2018 |
AAAI |
Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions |
67 |
| 2017 |
EMNLP |
A causal framework for explaining the predictions of black-box sequence-to-sequence models |
64 |
| 2017 |
CEURW |
What does explainable AI really mean? A new conceptualization of perspectives |
64 |
| 2019 |
AAAI |
Interpretation of neural networks is fragile |
63 |
| 2019 |
ACL |
Attention is not Explanation |
57 |
| 2018 |
TPAMI |
Interpreting deep visual representations via network dissection |
56 |
| 2017 |
ACL |
Visualizing and Understanding Neural Machine Translation |
56 |
| 2019 |
NMI |
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead |
54 |
| 2019 |
ACMFAT |
Explaining explanations in AI |
54 |
| 2018 |
CVPR |
Transparency by design: Closing the gap between performance and interpretability in visual reasoning |
54 |
| 2018 |
AAAI |
Interpreting CNN Knowledge via an Explanatory Graph |
54 |
| 2018 |
MIPRO |
Explainable artificial intelligence: A survey |
54 |
| 2019 |
CVPR |
Interpreting CNNs via Decision Trees |
49 |
| 2017 |
survey |
Interpretability of deep learning models: a survey of results |
49 |
| 2018 |
ICML |
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples |
47 |
| 2019 |
CVPR |
From Recognition to Cognition: Visual Commonsense Reasoning |
44 |
| 2017 |
arxiv |
Towards interpretable deep neural networks by leveraging adversarial examples |
44 |
| 2017 |
CVPR |
Improving Interpretability of Deep Neural Networks with Semantic Information |
43 |
| 2016 |
arxiv |
Attentive Explanations: Justifying Decisions and Pointing to the Evidence |
41 |
| 2018 |
ECCV |
Explainable neural computation via stack neural module networks |
40 |
| 2018 |
CVPR |
Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks |
39 |
| 2017 |
ICCV |
Understanding and comparing deep neural networks for age and gender classification |
39 |
| 2018 |
ECCV |
Grounding visual explanations |
38 |
| 2019 |
NIPS |
This looks like that: deep learning for interpretable image recognition |
35 |
| 2018 |
NIPS |
Explanations based on the missing: Towards contrastive explanations with pertinent negatives |
35 |
| 2017 |
IJCAI |
Understanding and improving convolutional neural networks via concatenated rectified linear units |
35 |
| 2018 |
ACL |
Did the Model Understand the Question? |
34 |
| 2018 |
ICLR |
Detecting statistical interactions from neural network weights |
30 |
| 2018 |
ECCV |
Textual explanations for self-driving vehicles |
30 |
| 2018 |
BMVC |
Rise: Randomized input sampling for explanation of black-box models |
30 |
| 2017 |
arxiv |
Contextual Explanation Networks |
28 |
| 2016 |
ICML |
Visualizing and comparing AlexNet and VGG using deconvolutional layers |
28 |
| 2018 |
NIPS |
Towards robust interpretability with self-explaining neural networks |
27 |
| 2018 |
AIES |
Detecting Bias in Black-Box Models Using Transparent Model Distillation |
27 |
| 2018 |
arxiv |
How convolutional neural network see the world-A survey of convolutional neural network visualization methods |
27 |
| 2018 |
ECCV |
Interpretable basis decomposition for visual explanation |
26 |
| 2018 |
NIPS |
Attacks meet interpretability: Attribute-steered detection of adversarial samples |
26 |
| 2017 |
ICLR |
Exploring LOTS in Deep Neural Networks |
26 |
| 2017 |
AAAI |
Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning |
26 |
| 2018 |
arxiv |
Revisiting the importance of individual units in cnns via ablation |
25 |
| 2018 |
AAAI |
Examining CNN Representations with respect to Dataset Bias |
24 |
| 2016 |
arxiv |
Investigating the influence of noise and distractors on the interpretation of neural networks |
24 |
| 2016 |
IJCV |
Visualizing Object Detection Features |
22 |
| 2018 |
ICLR |
Interpretable counting for visual question answering |
21 |
| 2018 |
CVPR |
What have we learned from deep representations for action recognition? |
20 |
| 2018 |
CVPR |
Learning to Act Properly: Predicting and Explaining Affordances from Images |
17 |
| 2018 |
ECCV |
Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases |
17 |
| 2018 |
NIPS Workshop |
Interpretable Convolutional Filters with SincNet |
17 |
| 2019 |
JVCIR |
Interpretable convolutional neural networks via feedforward design |
16 |
| 2019 |
ICLR |
Hierarchical interpretations for neural network predictions |
15 |
| 2018 |
NIPS |
DeepPINK: reproducible feature selection in deep neural networks |
15 |
| 2017 |
CVPR |
Mining Object Parts from CNNs via Active Question-Answering |
15 |
| 2019 |
CVPR |
Attention branch network: Learning of attention mechanism for visual explanation |
14 |
| 2017 |
CVPRW |
Looking under the hood: Deep neural network visualization to interpret whole-slide image analysis outcomes for colorectal polyps |
14 |
| 2018 |
CVPR |
Teaching Categories to Human Learners with Visual Explanations |
13 |
| 2018 |
ECCV |
Vqa-e: Explaining, elaborating, and enhancing your answers for visual questions |
12 |
| 2018 |
NIPS |
Representer point selection for explaining deep neural networks |
11 |
| 2016 |
ECCV |
Design of kernels in convolutional neural networks for image classification |
11 |
| 2019 |
ICLR |
How Important Is a Neuron? |
10 |
| 2017 |
ICCV |
Learning to disambiguate by asking discriminative questions |
10 |
| 2019 |
AAAIW |
Unsupervised Learning of Neural Networks to Explain Neural Networks |
9 |
| 2018 |
CVPR |
What do Deep Networks Like to See? |
9 |
| 2019 |
CVPR |
Interpretable and fine-grained visual explanations for convolutional neural networks |
8 |
| 2018 |
ECCV |
Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance |
8 |
| 2019 |
ICLR |
Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks |
7 |
| 2019 |
ICAIS |
Interpreting black box predictions using fisher kernels |
7 |
| 2019 |
CVPR |
Learning to Explain with Complemental Examples |
6 |
| 2019 |
ICCV |
U-CAM: Visual Explanation using Uncertainty based Class Activation Maps |
6 |
| 2019 |
ICCV |
Towards Interpretable Face Recognition |
6 |
| 2019 |
CVPR |
Revealing Scenes by Inverting Structure from Motion Reconstructions |
5 |
| 2019 |
ICCV |
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded |
5 |
| 2018 |
CVPR |
Interpret Neural Networks by Identifying Critical Data Routing Paths |
5 |
| 2018 |
ECCV |
Diverse feature visualizations reveal invariances in early layers of deep neural networks |
5 |
| 2019 |
ICML |
Towards A Deep and Unified Understanding of Deep Neural Models in NLP |
4 |
| 2019 |
AAAI |
Classifier-agnostic saliency map extraction |
4 |
| 2019 |
AAAIW |
Network Transplanting |
4 |
| 2019 |
arxiv |
Attention Interpretability Across NLP Tasks |
4 |
| 2019 |
NIPS |
A benchmark for interpretability methods in deep neural networks（同arxiv:1806.10758） |
3 |
| 2019 |
arxiv |
Interpretable CNNs |
3 |
| 2019 |
NIPS |
Full-gradient representation for neural network visualization |
2 |
| 2019 |
NIPS |
On the (In) fidelity and Sensitivity of Explanations |
2 |
| 2019 |
ICCV |
Understanding Deep Networks via Extremal Perturbations and Smooth Masks |
2 |
| 2019 |
NIPS |
Towards Automatic Concept-based Explanations |
1 |
| 2019 |
NIPS |
CXPlain: Causal explanations for model interpretation under uncertainty |
1 |
| 2019 |
CVPR |
Multimodal Explanations by Predicting Counterfactuality in Videos |
1 |
| 2019 |
CVPR |
Visualizing the Resilience of Deep Convolutional Network Interpretations |
1 |
| 2019 |
ICCV |
Explaining Neural Networks Semantically and Quantitatively |
1 |
| 2018 |
arxiv |
Computationally Efficient Measures of Internal Neuron Importance |
1 |
| 2020 |
ICLR |
Knowledge Isomorphism between Neural Networks |
0 |
| 2020 |
ICLR |
Interpretable Complex-Valued Neural Networks for Privacy Protection |
0 |
| 2019 |
AAAI |
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval |
0 |
| 2018 |
ECCV |
ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations |
0 |