Domain-Adversarial Training of Neural Networks
本篇是遷移學習專欄介紹的第十八篇論文，發表在JMLR2016上。 Abstrac 提出了一種新的領域適應表示學習方法，即訓練和測試時的數據來自相似但不同的分佈。我們的方法直接受到域適應理論的啓發，該理論認爲，要實現有效的域轉移，必須基於不能區分訓練(源)域和測試(目標)域的特徵進行預測。 該方法在神經網絡體系結構的上下文中實現了這一思想，神經網絡體系結構的訓練對象是源域的標記數據和目標域的未標記數
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相關文章
- 1. DANN：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