论文笔记：Exploring Neural Networks with Activation Atlases
主要内容 本文在spatial activation visualization的基础上，通过对每个样本的activation vector进行数据降维到二维空间中，并且使用归因向量平均（average the attribution vectors）和特征反转（feature visualization）的方法，得到了反映出模型对于整个数据集的特征关注情况的激活图谱（activation atl
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相关文章
- 1. 论文笔记 - kernel-based non-parametric activation functions for neural networks
- 2. 【Binarized Neural Networks】论文笔记
- 3. 【论文阅读笔记】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 论文笔记
- 6. BinaryConnect: Training Deep Neural Networks with binary weights during propagations 论文笔记
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- 8. [2018][ICLR]FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 论文笔记
- 9. Bag of Tricks for Image Classification with Convolutional Neural Networks论文笔记
- 10. 06论文笔记《Reducing the Dimensionality of Data with Neural Networks》