Test-Time Adaptation with State-Space Models

• URL: http://arxiv.org/abs/2407.12492v1
• Date: Wed, 17 Jul 2024 11:18:49 GMT
• Title: Test-Time Adaptation with State-Space Models
• Authors: Mona Schirmer, Dan Zhang, Eric Nalisnick,
• Abstract summary: We propose a state-space model that can adapt a deployed model subjected to distribution drift. Our model learns the dynamics induced by distribution shifts on the last set of hidden features. Our approach is lightweight, modifying only the model's last linear layer.
• Score: 4.248760709042802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distribution shifts between training and test data are all but inevitable over the lifecycle of a deployed model and lead to performance decay. Adapting the model can hopefully mitigate this drop in performance. Yet, adaptation is challenging since it must be unsupervised: we usually do not have access to any labeled data at test time. In this paper, we propose a probabilistic state-space model that can adapt a deployed model subjected to distribution drift. Our model learns the dynamics induced by distribution shifts on the last set of hidden features. Without requiring labels, we infer time-evolving class prototypes that serve as a dynamic classification head. Moreover, our approach is lightweight, modifying only the model's last linear layer. In experiments on real-world distribution shifts and synthetic corruptions, we demonstrate that our approach performs competitively with methods that require back-propagation and access to the model backbone. Our model especially excels in the case of small test batches - the most difficult setting.

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