Deep networks rely on massive amounts of labeled data to learn powerful models. For a target task short of labeled data, transfer learning enables model adaptation from a different source domain. This paper addresses deep transfer learning under a more general scenario that the joint distributions of features and labels may change substantially across domains. Based on the theory of Hilbert space embedding of distributions, a novel joint distribution discrepancy is proposed to directly compare joint distributions across domains, eliminating the need of marginal-conditional factorization. Transfer learning is enabled in deep convolutional networks, where the dataset shifts may linger in multiple task-specific feature layers and the classifier layer. A set of joint adaptation networks are crafted to match the joint distributions of these layers across domains by minimizing the joint distribution discrepancy, which can be trained efficiently using back-propagation. Experiments show that the new approach yields state of the art results on standard domain adaptation datasets.

本研究介绍了一种叫做JAN的联合适应网络，它通过最大均值偏差准则（JMMD）对多个特定领域层次的联合分布进行对齐，实现从源领域到目标领域的迁移学习，实验表明我们的模型在标准数据集上提供了最先进的结果。

联合适应网络的深度迁移学习