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* A Survey Of Methods For Explaining Black Box Models. _Guidotti et al. 2018_ [pdf](https://arxiv.org/pdf/1802.01933.pdf)
# Visualizing Preferred Stimuli
## Activation Maximization
### Activation Maximization
* 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)
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* Feature Visualization. _Olah et al. 2017_ [url](https://distill.pub/2017/feature-visualization)
* Diverse feature visualizations reveal invariances in early layers of deep neural networks. _Cadena et al. 2018_ [pdf](https://arxiv.org/pdf/1807.10589.pdf)
## Real images / Segmentation Masks
### 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
### White-box
* 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)
* __CAM__: Learning Deep Features for Discriminative Localization. _Zhou et al. 2016_ [code](https://github.com/metalbubble/CAM) | [web](http://cnnlocalization.csail.mit.edu/)
* __Grad-CAM__: Visual Explanations from Deep Networks via Gradient-based Localization. _Selvaraju et al. 2017_ [pdf](https://arxiv.org/abs/1610.02391)
* __Grad-CAM++__: Improved Visual Explanations for Deep Convolutional Networks. _Chattopadhyay et al. 2017_ [pdf](https://arxiv.org/abs/1710.11063) | [code](https://github.com/adityac94/Grad_CAM_plus_plus)
* __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)
* CAM: Learning Deep Features for Discriminative Localization. _Zhou et al. 2016_ [code](https://github.com/metalbubble/CAM) | [web](http://cnnlocalization.csail.mit.edu/)
* Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. _Selvaraju et al. 2017_ [pdf](https://arxiv.org/abs/1610.02391)
* Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks. _Chattopadhyay et al. 2017_ [pdf](https://arxiv.org/abs/1710.11063) | [code](https://github.com/adityac94/Grad_CAM_plus_plus)
* 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)
* Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks. _Seo et al. 2018_ [pdf](https://arxiv.org/pdf/1807.11720.pdf)
* Integrated Gradients: Axiomatic Attribution for Deep Networks. _Sundararajan et al. 2018_ [pdf](http://proceedings.mlr.press/v70/sundararajan17a/sundararajan17a.pdf) | [code](https://github.com/ankurtaly/Integrated-Gradients)
* The (Un)reliability of saliency methods. _Kindermans et al. 2018_ [pdf](https://openreview.net/forum?id=r1Oen--RW)
* Sanity Checks for Saliency Maps. _Adebayo et al. 2018_ [pdf](http://papers.nips.cc/paper/8160-sanity-checks-for-saliency-maps.pdf)
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* Deep Learning for Case-Based Reasoning through Prototypes [pdf](https://arxiv.org/pdf/1710.04806.pdf)
* Unsupervised Learning of Neural Networks to Explain Neural Networks [pdf](https://arxiv.org/abs/1805.07468)
## Machine rationalization
### Machine rationalization
* Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions [pdf](https://arxiv.org/abs/1901.03729)
* Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations [pdf](https://arxiv.org/pdf/1702.07826.pdf)