On f -Divergence Principled Domain Adaptation: An Improved Framework

Ziqiao Wang, Yongyi Mao

arXiv · PDF · Open in the Atlas

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Repositories linked to this paper

• ziqiaowanggeothe/f-dd — marked official in Papers with Code; named in the paper
• ZiqiaoWangGeothe/f-DD
• nv-tlabs/fDAL
• thuml/CDAN
• thuml/MDD

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Abstract

Unsupervised domain adaptation (UDA) plays a crucial role in addressing distribution shifts in machine learning. In this work, we improve the theoretical foundations of UDA proposed in Acuna et al. (2021) by refining their f -divergence-based discrepancy and additionally introducing a new measure, f -domain discrepancy (f -DD). By removing the absolute value function and incorporating a scaling parameter, f -DD obtains novel target error and sample complexity bounds, allowing us to recover previous KL-based results and bridging the gap between algorithms and theory presented in Acuna et al. (2021). Using a localization technique, we also develop a fast-rate generalization bound. Empirical results demonstrate the superior performance of f -DD-based learning algorithms over previous works in popular UDA benchmarks.

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