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@ -78,8 +78,9 @@ This is an on-going attempt to consolidate interesting efforts in the area of un
* SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. _Raghu et al. 2017_ [pdf](https://arxiv.org/abs/1706.05806) | [code](https://github.com/google/svcca)
* A Peek Into the Hidden Layers of a Convolutional Neural Network Through a Factorization Lens. _Saini et al. 2018_ [pdf](https://arxiv.org/abs/1806.02012)
* Network Dissection: Quantifying Interpretability of Deep Visual Representations. _Bau et al. CVPR 2017_ [url](http://netdissect.csail.mit.edu/) | [pdf](http://netdissect.csail.mit.edu/final-network-dissection.pdf)
* GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. _Bau et al. 2018_ [pdf](https://arxiv.org/abs/1811.10597)
* Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks. _Fong & Vedaldi 2018_ [pdf](https://arxiv.org/abs/1801.03454)
* GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. _Bau et al. ICLR 2019_ [pdf](https://arxiv.org/abs/1811.10597)
* Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks. _Fong & Vedaldi CVPR 2018_ [pdf](https://arxiv.org/abs/1801.03454)
* Intriguing generalization and simplicity of adversarially trained neural networks. _Agarwal, Chen, Nguyen 2020_ [pdf](http://anhnguyen.me/project/generalization-simplicity-robust-networks/)
## A5. Network surgery
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* Counterfactual Visual Explanations. _Goyal et al. 2019_ [pdf](https://arxiv.org/pdf/1904.07451.pdf)
* Generative Counterfactual Introspection for Explainable Deep Learning. _Liu et al. 2019_ [pdf](https://arxiv.org/abs/1907.03077)
### Generative models
* Generative causal explanations of black-box classifiers. _O’Shaughnessy et al. 2020_ [pdf](https://arxiv.org/abs/2006.13913)
* Removing input features via a generative model to explain their attributions to classifier's decisions. _Agarwal et al. 2019_ [pdf](https://arxiv.org/abs/1910.04256) | [code](https://github.com/anguyen8/generative-attribution-methods)
# D. Others
* Yang, S. C. H., & Shafto, P. Explainable Artificial Intelligence via Bayesian Teaching. NIPS 2017 [pdf](http://shaftolab.com/assets/papers/yangShafto_NIPS_2017_machine_teaching.pdf)
* Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation [pdf](http://www.antoniosliapis.com/papers/explainable_ai_for_designers.pdf)