Revisiting Realistic Test-Time Training: Sequential Inference and
Adaptation by Anchored Clustering Regularized Self-Training
- URL: http://arxiv.org/abs/2303.10856v1
- Date: Mon, 20 Mar 2023 04:30:18 GMT
- Title: Revisiting Realistic Test-Time Training: Sequential Inference and
Adaptation by Anchored Clustering Regularized Self-Training
- Authors: Yongyi Su, Xun Xu, Tianrui Li, Kui Jia
- Abstract summary: We develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning.
Self-training(ST) has demonstrated great success in learning from unlabeled data.
TTAC++ consistently outperforms the state-of-the-art methods on five TTT datasets.
- Score: 37.75537703971045
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deploying models on target domain data subject to distribution shift requires
adaptation. Test-time training (TTT) emerges as a solution to this adaptation
under a realistic scenario where access to full source domain data is not
available, and instant inference on the target domain is required. Despite many
efforts into TTT, there is a confusion over the experimental settings, thus
leading to unfair comparisons. In this work, we first revisit TTT assumptions
and categorize TTT protocols by two key factors. Among the multiple protocols,
we adopt a realistic sequential test-time training (sTTT) protocol, under which
we develop a test-time anchored clustering (TTAC) approach to enable stronger
test-time feature learning. TTAC discovers clusters in both source and target
domains and matches the target clusters to the source ones to improve
adaptation. When source domain information is strictly absent (i.e.
source-free) we further develop an efficient method to infer source domain
distributions for anchored clustering. Finally, self-training~(ST) has
demonstrated great success in learning from unlabeled data and we empirically
figure out that applying ST alone to TTT is prone to confirmation bias.
Therefore, a more effective TTT approach is introduced by regularizing
self-training with anchored clustering, and the improved model is referred to
as TTAC++. We demonstrate that, under all TTT protocols, TTAC++ consistently
outperforms the state-of-the-art methods on five TTT datasets, including
corrupted target domain, selected hard samples, synthetic-to-real adaptation
and adversarially attacked target domain. We hope this work will provide a fair
benchmarking of TTT methods, and future research should be compared within
respective protocols.
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