对于DAN方法的解读-Learning Transferable Features with Deep Adaptation Networks
上周汇报了该篇经典文章，现在回顾并且记录一下自己对DAN方法的理解 深度适配网络-DAN 《利用深度适应网络学习可迁移特征》 下面分为五个部分来讲解： 一. 研究背景 二. 本论文所解决的问题 三. DAN 方法 四. 实验部分 五. 结合自己的论文 一.研究背景 精简的说， 研究表明：深度 神经网络可以学习可迁移特征，这些特征用于域适应时在新的任务上表现出很好的泛化能力。然而由于深度特征随着网
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相关文章
- 1. Learning Transferable Features with Deep Adaptation Networks
- 2. 翻译“Learning Transferable Features with Deep Adaptation Networks”
- 3. How transferable are features in deep nerual networks? 【paper review】
- 4. How transferable are features in deep neural networks?
- 5. [解读] Deep Clustering for Unsupervised Learning of Visual Features
- 6. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）
- 7. Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning
- 8. How transferable are features in deep neural networks? 论文笔记
- 9. 论文笔记：How transferable are features in deep neural networks?
- 10. 论文阅读笔记（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）