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.

该研究解决了测试时领域适应中，预训练模型在有限无标签目标数据上表现不佳的问题。提出了一种元学习最小最大框架，使自监督学习任务与主要任务对齐，同时减少小批量过拟合。研究表明，该方法显著提升了模型在未知领域的鲁棒性，并在多个领域适应和泛化基准测试中超过了最先进技术。

Meta-TTT：一种用于测试时训练的元学习最小最大框架