Awesome-XAI

1. Visualization of CNN representations

1-1. Gradient-based method

Compute gradient of the score or intermediate neuron with respect to the input image.

• Feature Visualization [page]
  Olah et al. 2017
• Understanding Neural Networks Through Deep Visualization [paper]
  Jason Yosinski et al., 2015
• Striving for Simplicity: The All Convolutional Net [paper]
  Jost Tobias Springenberg et al. 2015
• Understanding Deep Image Representations by Inverting Them [paper]
  Aravindh Mahendran et al., 2015
• Deep Inside Convolutional Network: Visualising Image Classification Models and Saliency Maps [paper]
  Karen Simonyan et al., 2013
• Visualizing and Understanding Convolutional Networks [paper]
  Matthew D Zeiler et al., 2013

1-2. Feature maps: Visualization, Interpretation

• Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space [paper]
  Anh Nguyen et al., 2017
• Inverting Visual Representations with Convolutional Networks [paper]
  Alexey Dosovitskiy et al., 2016
• Object Detectors Emerge in Deep Scene CNNs [paper]
  Bolei Zhou et al., 2015

2. Diagnosis of CNN representations

2-1. Analyzing CNN features

• Understanding Deep Features with Computer-generated Imagery [paper]
  Mathieu Aubry et al., 2015
• How Transferable are Features in Deep Neural Networks [paper]
  Jason Yosinski et al., 2014
• Going Deeper with Convolutions [paper]
  Christian Szegedy et al., 2014

2-2. Extracting image regions from the network output

• Interpretable Explanations of Black Boxes by Meaningful Perturbation [paper]
  Ruth Fong et al., 2017
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization [paper]
  Ramprasaath R. Selvaraju et al., 2017
• Visualizing Deep Neural Network Decisions: Prediction Difference Analysis [paper]
  Luisa M Zintgraf et al., 2017
• The (Un)reliability of saliency methods [paper]
  Pieter-Jan Kindermans et al., 2017
• "Why Should I Trust You?": Explaining the Predictions of Any Classifier [paper]
  Marco Tulio Ribeiro et al., 2016

2-3. Vulnerability of Deep Neural Net Model

• Understanding Block-box Predictions via Influence Functions [paper]
  Pang Wei Koh et al., 2017
• One Pixel Attack for Fooling Deep Neural Networks [paper]
  Jiawei Su et al., 2017

2-4. Refining Network Representations

• Harnessing Deep Neural Networks with Logic Rules [paper]
  Zhiting Hu et al., 2016

2-5. Discovering Bugs in Neural Networks

• Examing CNN Representations with respect to Dataset Bias [paper]
  Quanshi Zhang et al., 2017

3. Disentangling CNN representations into

• Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning [paper]
  Quanshi Zhang et al.,2016
• Interpreting CNN Knowledge Via An Explanatory Graph [paper]
  Quanshi Zhang et al.,2018
• Interpreting CNNs via Decision Trees [paper]
  Quanshi Zhang et al.,2018
• Interpret Neural Networks by Identifying Critical Data Routing Paths [paper]
  Yulong Wang et al.,2018

4. Building explainable models

Modifying model structure to interpret model

• Interpretable Convolution Neural Networks [paper]
  Quanshi Zhang et al.,2018
• Towards Interpretable R-CNN by Unfolding Latent Structures [paper] Tianfu Wu et al.,2018
• Dynamic Routing Between Capsules [paper]
  Sara Sabour et al.,2017
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets [paper]
  Xi Chen et al.,2016

5. Evaluation metrics for network interpretability

5-1. Filter interpretability

• Network Dissection: Quantifying Interpretability of Deep Visual Representations [paper]
  David Bau et al., 2017

5-2. Location instability

• Interpreting CNN Knowledge Via An Explanatory Graph [paper]
  Quanshi Zhang et al.,2018

99. Survey Papers

• Visual Interpretability for Deep Learning: a Survey [paper]
  Quanshi Zhang,Song-Chun Zhu, 2018