DANN：Domain-Adversarial Training of Neural Networks
DANN原理理解 DANN中源域和目标域经过相同的映射来实现对齐。 DANN的目标函数分为两部分： 1. 源域分类损失项 2. 源域和目标域域分类损失项 1.源域分类损失项 对于一个m维的数据点X，通过一个隐含层Gf，数据点变为D维： 然后经过一个分类层Gy得到分类结果： 这个点的源域分类损失定义为：
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相关文章
- 1. Domain-Adversarial Training of Neural Networks
- 2. Xavier——Understanding the difficulty of training deep feedforward neural networks
- 3. On the difficulty of training Recurrent Neural Networks
- 4. Bag of Freebies for Training Object Detection Neural Networks
- 5. Training Neural Networks, part I
- 6. [cv231n] Lecture 7 | Training Neural Networks II
- 7. （转）A Recipe for Training Neural Networks
- 8. STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS
- 9. Applications of Graph Neural Networks
- 10. CS231N-6&7-Training Neural Networks