Partial Transfer Learning with Selective Adversarial Networks
本篇是遷移學習專欄介紹的第十一篇論文，清華大學助理教授Mingsheng Long (龍明盛)發表在國際機器學習頂級會議CVPR2018。 Abstract 對抗性學習已經成功地嵌入到深度網絡中，以學習可遷移的特徵，從而減少源域和目標域之間的分佈差異。現有的域對抗網絡假設跨域完全共享標籤空間。在大數據的存在下，將分類和表示模型從現有的大規模域遷移到未知的小規模域具有很強的動力。引入部分遷移學習，將
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相關文章
- 1. Partial Transfer Learning with Selective Adversarial Networks
- 2. 遷移學習「Partial Transfer Learning with Selective Adversarial Networks」
- 3. CVPR 2018之遷移學習解讀（二）： Partial Transfer Learning with Selective Adversarial Networks
- 4. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 5. DCGAN：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- 6. [advGAN]Generating Adversarial Examples With Adversarial Networks
- 7. Generating Adversarial Examples with Adversarial Networks
- 8. partial transfer learning文章總結(三)
- 9. 【Transfer Learning】Adversarial Discriminative Domain Adaptation
- 10. transfer learning