DANN：Domain-Adversarial Training of Neural Networks
DANN原理理解 DANN中源域和目標域經過相同的映射來實現對齊。 DANN的目標函數分爲兩部分： 1. 源域分類損失項 2. 源域和目標域域分類損失項 1.源域分類損失項 對於一個m維的數據點X，通過一個隱含層Gf，數據點變爲D維： 然後經過一個分類層Gy得到分類結果： 這個點的源域分類損失定義爲：
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相關文章
- 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. （轉）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