論文筆記：Exploring Neural Networks with Activation Atlases
主要內容 本文在spatial activation visualization的基礎上，通過對每個樣本的activation vector進行數據降維到二維空間中，並且使用歸因向量平均（average the attribution vectors）和特徵反轉（feature visualization）的方法，得到了反映出模型對於整個數據集的特徵關注情況的激活圖譜（activation atl
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相關文章
- 1. 論文筆記 - kernel-based non-parametric activation functions for neural networks
- 2. 【Binarized Neural Networks】論文筆記
- 3. 【論文閱讀筆記】PACT：PArameterized Clipping Activation for Quantized Neural Networks
- 4. Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
- 5. Understanding Neural Networks Through Deep Visualization 論文筆記
- 6. BinaryConnect: Training Deep Neural Networks with binary weights during propagations 論文筆記
- 7. 論文筆記：ImageNet Classification with Deep Convolutional Neural Networks
- 8. [2018][ICLR]FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 論文筆記
- 9. Bag of Tricks for Image Classification with Convolutional Neural Networks論文筆記
- 10. 06論文筆記《Reducing the Dimensionality of Data with Neural Networks》