For a target task where labeled data is unavailable, domain adaptation can
transfer a learner from a different source domain. Previous deep domain
adaptation methods mainly learn a global domain shift, i.e., align the global
source and target distributions without considering the relationships between
two subdomains within the same category of different domains, leading to
unsatisfying transfer learning performance without capturing the fine-grained
information. Recently, more and more researchers pay attention to Subdomain
Adaptation which focuses on accurately aligning the distributions of the
relevant subdomains. However, most of them are adversarial methods which
contain several loss functions and converge slowly. Based on this, we present
Deep Subdomain Adaptation Network (DSAN) which learns a transfer network by
aligning the relevant subdomain distributions of domain-specific layer
activations across different domains based on a local maximum mean discrepancy
(LMMD). Our DSAN is very simple but effective which does not need adversarial
training and converges fast. The adaptation can be achieved easily with most
feed-forward network models by extending them with LMMD loss, which can be
trained efficiently via back-propagation. Experiments demonstrate that DSAN can
achieve remarkable results on both object recognition tasks and digit
classification tasks. Our code will be available at:
this https URL

该研究提出了一种基于 Deep Subdomain Adaptation Network (DSAN) 的领域自适应方法，通过使用本地最大平均差异 (LMMD) 对不同领域的相关子域分布进行对齐，从而不需要敌对训练且收敛速度快，在对象识别和数字分类任务中均取得了显着的结果。