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Papers on Explainable Artificial Intelligence
This is an on-going attempt to consolidate all interesting efforts in the area of understanding / interpreting / explaining / visualizing machine learning models.
GUI tools
Surveys
- Methods for Interpreting and Understanding Deep Neural Networks. Montavon et al. 2017 pdf
- The Mythos of Model Interpretability. Lipton 2016 pdf
- Towards A Rigorous Science of Interpretable Machine Learning Doshi-Velez & Kim. 2017 pdf
- Visualizations of Deep Neural Networks in Computer Vision: A Survey. Seifert et al. 2017 pdf
- How convolutional neural network see the world - A survey of convolutional neural network visualization methods. Qin et al. 2018 pdf
- A brief survey of visualization methods for deep learning models from the perspective of Explainable AI. Chalkiadakis 2018 pdf
Visualizing Preferred Stimuli
Activation Maximization
- Visualizing higher-layer features of a deep network. Erhan et al. 2009 pdf
- Understanding Neural Networks through Deep Visualization. Yosinski et al. 2015 pdf | url
- Multifaceted Feature Visualization: Uncovering the different types of features learned by each neuron in deep neural networks. Nguyen et al. 2016 pdf | code
- Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. Nguyen et al. 2016 pdf | code
- Plug and Play Generative Networks. Nguyen et al. 2017 pdf | code
- Feature Visualization. Olah et al. 2017 url
Real images / Segmentation Masks
- Object Detectors Emerge in Deep Scene CNNs. Zhou et al. 2015 pdf
- Network Dissection: Quantifying Interpretability of Deep Visual Representations. Bau et al. 2017 url | pdf
- Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks. Fong & Vedaldi 2018 pdf
Heatmaps / Attribution
- Learning how to explain neural networks: PatternNet and PatternAttribution pdf
- A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations pdf
- A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks pdf
- How Important Is a Neuron? Dhamdhere et al. 2018 pdf
Layer-wise Backpropagation
- Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation pdf
- Explaining NonLinear Classification Decisions With Deep Tayor Decomposition pdf
Inverting Neural Networks
- Understanding Deep Image Representations by Inverting Them pdf
- Inverting Visual Representations with Convolutional Networks pdf
- Neural network inversion beyond gradient descent pdf
Bayesian approaches
- Yang, S. C. H., & Shafto, P. Explainable Artificial Intelligence via Bayesian Teaching. NIPS 2017 pdf
Distilling DNNs into more interpretable models
Learning to explain
- Deep Learning for Case-Based Reasoning through Prototypes pdf
- Unsupervised Learning of Neural Networks to Explain Neural Networks pdf