Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge
  Transfer
Liyue Chen Linian Wang Jinyu Xu Shuai Chen Weiqiang Wang Wenbiao Zhao Qiyu Li Leye Wang
Most state-of-the-art deep domain adaptation techniques align source and
target samples in a global fashion. That is, after alignment, each source
sample is expected to become similar to any target sample. However, global
alignment may not always be optimal or necessary in practice. For example,
consider cross-domain fraud detection, where there are two types of
transactions: credit and non-credit. Aligning credit and non-credit
transactions separately may yield better performance than global alignment, as
credit transactions are unlikely to exhibit patterns similar to non-credit
transactions. To enable such fine-grained domain adaption, we propose a novel
Knowledge-Inspired Subdomain Adaptation (KISA) framework. In particular, (1) We
provide the theoretical insight that KISA minimizes the shared expected loss
which is the premise for the success of domain adaptation methods. (2) We
propose the knowledge-inspired subdomain division problem that plays a crucial
role in fine-grained domain adaption. (3) We design a knowledge fusion network
to exploit diverse domain knowledge. Extensive experiments demonstrate that
KISA achieves remarkable results on fraud detection and traffic demand
prediction tasks.