[2M5-OS-24] OS-24

[2M5-OS-24-03] Domain Adaptation for Extra Features Using Fused Gromov-Wasserstein Based on Graph Distance

〇Toshimitsu Aritake¹, Hideitsu Hino^2,3 (1. Hitotsubashi University, 2. The Institute of Statistical Mathematics, 3. RIKEN AIP)

Keywords:Transfer learning, Optimal transport, Domain adaptation

The main objective of domain adaptation is to transfer the knowledge of labeled training data obtained in the source domain to the target domain to learn a predictive model that performs well on the test data in the target domain.
In this study, we focus on the domain adaptation problem wherein the observation of additional features in the target domain is a domain shift.
We address this problem using fused Gromov--Wasserstein optimal transport, which concurrently solves standard optimal transport and Gromov--Wasserstein optimal transport.
We modified the definition of the source and target distance metric in Gromov--Wasserstein optimal transport so that the data with the same class label are clustered together in the target domain.
Specifically, we incorporated the label discrepancy into the source distance metric, and the distance on the graph, estimated from the test data, is used as the target distance metric.
Our proposed method more accurately estimates the target label by Fused Gromov--Wasserstein optimal transport using the structure information obtained from the test data.