Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training

• URL: http://arxiv.org/abs/2410.01709v1
• Date: Wed, 2 Oct 2024 16:16:05 GMT
• Title: Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training
• Authors: Chen Tao, Li Shen, Soumik Mondal,
• Abstract summary: Test-time domain adaptation is a challenging task that aims to adapt a pre-trained model to limited, unlabeled target data during inference. This paper introduces a meta-learning minimax framework for test-time training on batch normalization layers.
• Score: 5.9631503543049895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Test-time domain adaptation is a challenging task that aims to adapt a pre-trained model to limited, unlabeled target data during inference. Current methods that rely on self-supervision and entropy minimization underperform when the self-supervised learning (SSL) task does not align well with the primary objective. Additionally, minimizing entropy can lead to suboptimal solutions when there is limited diversity within minibatches. This paper introduces a meta-learning minimax framework for test-time training on batch normalization (BN) layers, ensuring that the SSL task aligns with the primary task while addressing minibatch overfitting. We adopt a mixed-BN approach that interpolates current test batch statistics with the statistics from source domains and propose a stochastic domain synthesizing method to improve model generalization and robustness to domain shifts. Extensive experiments demonstrate that our method surpasses state-of-the-art techniques across various domain adaptation and generalization benchmarks, significantly enhancing the pre-trained model's robustness on unseen domains.

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