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# GUI tools
* Deep Visualization Toolbox. _Yosinski et al. 2015_ [code](https://github.com/yosinski/deep-visualization-toolbox) | [pdf](http://yosinski.com/deepvis)
* __DeepVis__ Deep Visualization Toolbox. _Yosinski et al. 2015_ [code](https://github.com/yosinski/deep-visualization-toolbox) | [pdf](http://yosinski.com/deepvis)
# Surveys
* Methods for Interpreting and Understanding Deep Neural Networks. _Montavon et al. 2017_ [pdf](https://arxiv.org/pdf/1706.07979.pdf)
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# Visualizing Preferred Stimuli
## Activation Maximization
* Visualizing higher-layer features of a deep network. _Erhan et al. 2009_ [pdf](https://www.researchgate.net/publication/265022827_Visualizing_Higher-Layer_Features_of_a_Deep_Network)
* Understanding Neural Networks through Deep Visualization. _Yosinski et al. 2015_ [pdf](http://yosinski.com/media/papers/Yosinski__2015__ICML_DL__Understanding_Neural_Networks_Through_Deep_Visualization__.pdf) | [url](http://yosinski.com/deepvis)
* Multifaceted Feature Visualization: Uncovering the different types of features learned by each neuron in deep neural networks. _Nguyen et al. 2016_ [pdf](http://www.evolvingai.org/files/mfv_icml_workshop_16.pdf) | [code](https://github.com/Evolving-AI-Lab/mfv)
* Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. _Nguyen et al. 2016_ [pdf](anhnguyen.me/project/synthesizing) | [code](https://github.com/Evolving-AI-Lab/synthesizing)
* Plug and Play Generative Networks. _Nguyen et al. 2017_ [pdf](anhnguyen.me/project/ppgn/) | [code](https://github.com/Evolving-AI-Lab/ppgn)
* __AM__ Visualizing higher-layer features of a deep network. _Erhan et al. 2009_ [pdf](https://www.researchgate.net/publication/265022827_Visualizing_Higher-Layer_Features_of_a_Deep_Network)
* __DeepVis__ Understanding Neural Networks through Deep Visualization. _Yosinski et al. 2015_ [pdf](http://yosinski.com/media/papers/Yosinski__2015__ICML_DL__Understanding_Neural_Networks_Through_Deep_Visualization__.pdf) | [url](http://yosinski.com/deepvis)
* __MFV___ Multifaceted Feature Visualization: Uncovering the different types of features learned by each neuron in deep neural networks. _Nguyen et al. 2016_ [pdf](http://www.evolvingai.org/files/mfv_icml_workshop_16.pdf) | [code](https://github.com/Evolving-AI-Lab/mfv)
* __DGN-AM__ Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. _Nguyen et al. 2016_ [pdf](anhnguyen.me/project/synthesizing) | [code](https://github.com/Evolving-AI-Lab/synthesizing)
* __PPGN__ Plug and Play Generative Networks. _Nguyen et al. 2017_ [pdf](anhnguyen.me/project/ppgn/) | [code](https://github.com/Evolving-AI-Lab/ppgn)
* Feature Visualization. _Olah et al. 2017_ [url](https://distill.pub/2017/feature-visualization)
## Real images / Segmentation Masks
* Object Detectors Emerge in Deep Scene CNNs. Zhou et al. 2015 [pdf](https://arxiv.org/abs/1412.6856)
* Network Dissection: Quantifying Interpretability of Deep Visual Representations. Bau et al. 2017 [url](http://netdissect.csail.mit.edu/) | [pdf](http://netdissect.csail.mit.edu/final-network-dissection.pdf)
* Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks. Fong & Vedaldi 2018 [pdf](https://arxiv.org/abs/1801.03454)
* __Network Dissection__ Quantifying Interpretability of Deep Visual Representations. Bau et al. 2017 [url](http://netdissect.csail.mit.edu/) | [pdf](http://netdissect.csail.mit.edu/final-network-dissection.pdf)
* __Net2Vec__ Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks. Fong & Vedaldi 2018 [pdf](https://arxiv.org/abs/1801.03454)
# Heatmaps / Attribution
* Learning how to explain neural networks: PatternNet and PatternAttribution [pdf](https://arxiv.org/abs/1705.05598)
* A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations. _Nie et al. 2018_ [pdf](https://arxiv.org/abs/1805.07039)
* A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks [pdf](https://arxiv.org/pdf/1606.07757.pdf)
* How Important Is a Neuron? _Dhamdhere et al._ 2018 [pdf](https://arxiv.org/pdf/1805.12233.pdf)
* LIME: Why should i trust you?: Explaining the predictions of any classifier. _Ribeiro et al. 2016_ [pdf](https://arxiv.org/pdf/1602.04938.pdf) | [blog](https://homes.cs.washington.edu/~marcotcr/blog/lime/)
* CAM
* GradCAM
* __LIME__ Why should i trust you?: Explaining the predictions of any classifier. _Ribeiro et al. 2016_ [pdf](https://arxiv.org/pdf/1602.04938.pdf) | [blog](https://homes.cs.washington.edu/~marcotcr/blog/lime/)
* __CAM__
* __GradCAM__
* Unreliable saliency maps
## Layer-wise Backpropagation
* Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation [pdf](https://arxiv.org/abs/1712.08268)
* Explaining NonLinear Classification Decisions With Deep Tayor Decomposition [pdf](https://arxiv.org/abs/1512.02479)
* __LRP__ Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation [pdf](https://arxiv.org/abs/1712.08268)
* __DTD__ Explaining NonLinear Classification Decisions With Deep Tayor Decomposition [pdf](https://arxiv.org/abs/1512.02479)
# Inverting Neural Networks
* Understanding Deep Image Representations by Inverting Them [pdf](https://arxiv.org/abs/1412.0035)