Partial Transfer Learning with Selective Adversarial Networks
本篇是迁移学习专栏介绍的第十一篇论文，清华大学助理教授Mingsheng Long (龙明盛)发表在国际机器学习顶级会议CVPR2018。 Abstract 对抗性学习已经成功地嵌入到深度网络中，以学习可迁移的特征，从而减少源域和目标域之间的分布差异。现有的域对抗网络假设跨域完全共享标签空间。在大数据的存在下，将分类和表示模型从现有的大规模域迁移到未知的小规模域具有很强的动力。引入部分迁移学习，将
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相关文章
- 1. Partial Transfer Learning with Selective Adversarial Networks
- 2. 迁移学习“Partial Transfer Learning with Selective Adversarial Networks”
- 3. CVPR 2018之迁移学习解读（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文章总结(三)
- 9. 【Transfer Learning】Adversarial Discriminative Domain Adaptation
- 10. transfer learning