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# Papers on Understanding and Explaining Neural Networks
# Papers on Explainable Artificial Intelligence
This is an on-going attempt to consolidate all interesting efforts in the area of understanding / interpreting / explaining / visualizing neural networks.
This is an on-going attempt to consolidate all interesting efforts in the area of understanding / interpreting / explaining / visualizing machine learning models.
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## 1. GUI tools
## 0. GUI tools
* Deep Visualization Toolbox [code](https://github.com/yosinski/deep-visualization-toolbox) | [pdf](http://yosinski.com/deepvis)
## 1. Surveys
* Methods for Interpreting and Understanding Deep Neural Networks [pdf](https://arxiv.org/pdf/1706.07979.pdf)
* The Mythos of Model Interpretability [pdf](https://arxiv.org/abs/1606.03490)
* Towards A Rigorous Science of Interpretable Machine Learning [pdf](https://arxiv.org/pdf/1702.08608.pdf)
* A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks [pdf](https://arxiv.org/pdf/1606.07757.pdf)
* Visualizations of Deep Neural Networks in Computer Vision: A Survey (Seifert et al. 2017) [pdf](https://link.springer.com/chapter/10.1007/978-3-319-54024-5_6)
* How convolutional neural network see the world - A survey of convolutional neural network visualization methods (Qin et al. 2018) [pdf](https://arxiv.org/abs/1804.11191)
* A brief survey of visualization methods for deep learning models from the perspective of Explainable AI (Chalkiadakis 2018) [pdf](https://www.macs.hw.ac.uk/~ic14/IoannisChalkiadakis_RRR.pdf)
## 2. Feature Visualization / Activation Maximization
* Synthesizing the preferred inputs for neurons in neural networks via deep generator networks [code](https://github.com/Evolving-AI-Lab/synthesizing) | [pdf](anhnguyen.me/project/synthesizing)
* Plug and Play Generative Networks [pdf](anhnguyen.me/project/ppgn/) | [code](https://github.com/Evolving-AI-Lab/ppgn)