Revisiting Source-Free Domain Adaptation: a New Perspective via Uncertainty Control
Keywords: Source-Free Domain Adaptation, Unsupervised Domain Adaptation
Abstract: Source-Free Domain Adaptation (SFDA) seeks to adapt a pre-trained source model to the target domain using only unlabeled target data, without access to the original source data. While current state-of-the-art (SOTA) methods rely on leveraging weak supervision from the source model to extract reliable information for self-supervised adaptation, they often overlook the uncertainty that arises during the transfer process. In this paper, we conduct a systematic and theoretical analysis of the uncertainty inherent in existing SFDA methods and demonstrate its impact on transfer performance through the lens of Distributionally Robust Optimization (DRO). Building upon the theoretical results, we propose a novel instance-dependent uncertainty control algorithm for SFDA. Our method is designed to quantify and exploit the uncertainty during the adaptation process, significantly improving the model performance. Extensive experiments on benchmark datasets and empirical analyses confirm the validity of our theoretical findings and the effectiveness of the proposed method.
This work offers new insights into understanding and advancing SFDA performance.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 8466
Loading