Update README.md

README.md

@@ -15,43 +15,42 @@ This is an on-going attempt to consolidate all interesting efforts in the area o
 * 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)
 
-# Feature Visualization
+# 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)
 * Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. _Nguyen et al. 2016_ [code](https://github.com/Evolving-AI-Lab/synthesizing) | [pdf](anhnguyen.me/project/synthesizing)
 * 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)
 
-## 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)
 
-## Heatmaps
+# Heatmaps
 * 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 [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)
 
-### Layer-wise Backpropagation
+## 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)
 
-## Inverting Neural Networks
+# Inverting Neural Networks
 * Understanding Deep Image Representations by Inverting Them [pdf](https://arxiv.org/abs/1412.0035)
 * Inverting Visual Representations with Convolutional Networks [pdf](https://arxiv.org/abs/1506.02753)
 * Neural network inversion beyond gradient descent [pdf](http://opt-ml.org/papers/OPT2017_paper_38.pdf)
 
-## Bayesian approaches
-
+# Bayesian approaches
 * 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)
 
-## Distilling DNNs into more interpretable models
+# Distilling DNNs into more interpretable models
 * Interpreting CNNs via Decision Trees [pdf](https://arxiv.org/abs/1802.00121)
 * Distilling a Neural Network Into a Soft Decision Tree [pdf](https://arxiv.org/abs/1711.09784)
 
-## DNNs that learn to explain
+# DNNs that learn to explain
 * 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)
 
-## Others
+# Others
 * Understanding Deep Architectures by Interpretable Visual Summaries [pdf](https://arxiv.org/pdf/1801.09103.pdf)