On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
Trung Quoc Phung, Trung Le, Long Tung Vuong, Toan Tran, Anh Tuan Tran, Hung Bui, Dinh Phung
Abstract
Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e.g., learning domain-invariant representations and its trade-off. However, it seems not the case for the multiple source DA and domain generalization (DG) settings which are remarkably more complicated and sophisticated due to the involvement of multiple source domains and potential unavailability of target domain during training. In this paper, we develop novel upper-bounds for the target general loss which appeal us to define two kinds of domain-invariant representations. We further study the pros and cons as well as the trade-offs of enforcing learning each domain-invariant representation. Finally, we conduct experiments to inspect the trade-off of these representations for offering practical hints regarding how to use them in practice and explore other interesting properties of our developed theory.
BibTeX
@inproceedings{
phung2021on,
title={On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources},
author={Trung Quoc Phung and Trung Le and Long Tung Vuong and Toan Tran and Anh Tuan Tran and Hung Bui and Dinh Phung},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=LkNBNOut0oD}
}