How transferable are features in deep neural networks?
1.背景和动机 本文全篇通过实验说明神经网络的可迁移性，通篇没有一个公式，但是却是2014年NIPS的oral presentation。 本文实验使用12年ImageNet大赛的冠军模型AlexNet进行了一系列关于网络各个层级上的可迁移性的实验。因为CNN在提取特征时，前面几层学习到的都是边边角角，线条这样的可以泛化的特征，而随着层级上升，慢慢地特征变得specific。那么问题来了，当我们打
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相关文章
- 1. How transferable are features in deep nerual networks? 【paper review】
- 2. How transferable are features in deep neural networks? 论文笔记
- 3. 论文笔记：How transferable are features in deep neural networks?
- 4. Learning Transferable Features with Deep Adaptation Networks
- 5. 翻译“Learning Transferable Features with Deep Adaptation Networks”
- 6. How powerful are graph neural networks?
- 7. HOW POWERFUL ARE GRAPH NEURAL NETWORKS?
- 8. How convolution neural networks work
- 9. 对于DAN方法的解读-Learning Transferable Features with Deep Adaptation Networks
- 10. Improving Deep Neural Networks