Online Feature Updates Improve Online (Generalized) Label Shift
  Adaptation
Ruihan Wu Siddhartha Datta Yi Su Dheeraj Baby Yu-Xiang Wang Kilian Q. Weinberger
This paper addresses the prevalent issue of label shift in an online setting
with missing labels, where data distributions change over time and obtaining
timely labels is challenging. While existing methods primarily focus on
adjusting or updating the final layer of a pre-trained classifier, we explore
the untapped potential of enhancing feature representations using unlabeled
data at test-time. Our novel method, Online Label Shift adaptation with Online
Feature Updates (OLS-OFU), leverages self-supervised learning to refine the
feature extraction process, thereby improving the prediction model. Theoretical
analyses confirm that OLS-OFU reduces algorithmic regret by capitalizing on
self-supervised learning for feature refinement. Empirical studies on various
datasets, under both online label shift and generalized label shift conditions,
underscore the effectiveness and robustness of OLS-OFU, especially in cases of
domain shifts.