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@ -13,11 +13,13 @@ This is an on-going attempt to consolidate all interesting efforts in the area o
## 3. Heatmap / 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 [pdf](https://arxiv.org/abs/1805.07039)
### 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)
## 4. 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)