标题： CTA：跨任务对齐以实现更好的测试时训练 显示英文标题

标题： CTA: Cross-Task Alignment for Better Test Time Training

摘要： 深度学习模型在广泛的计算机视觉任务中表现出色。 然而，当面对分布变化时，如领域或数据集的变化，其性能往往会显著下降。 测试时训练（TTT）已成为一种有效的方法，通过在训练期间引入一个辅助的无监督任务，并在测试时利用该任务进行模型更新，从而增强模型的鲁棒性。 在本工作中，我们引入了CTA（跨任务对齐），这是一种改进TTT的新方法。 与现有的TTT方法不同，CTA不需要专门的模型架构，而是从多模态对比学习的成功中获得灵感，以将监督编码器与自监督编码器对齐。 这一过程强制两种模型的学习表示之间对齐，从而减轻梯度干扰的风险，保留自监督学习的内在鲁棒性，并在测试时实现更具语义意义的更新。 实验结果表明，在多个基准数据集上，其鲁棒性和泛化能力均显著优于最先进的方法。 显示英文摘要

摘要： Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with distribution shifts, such as domain or dataset changes. Test-Time Training (TTT) has emerged as an effective method to enhance model robustness by incorporating an auxiliary unsupervised task during training and leveraging it for model updates at test time. In this work, we introduce CTA (Cross-Task Alignment), a novel approach for improving TTT. Unlike existing TTT methods, CTA does not require a specialized model architecture and instead takes inspiration from the success of multi-modal contrastive learning to align a supervised encoder with a self-supervised one. This process enforces alignment between the learned representations of both models, thereby mitigating the risk of gradient interference, preserving the intrinsic robustness of self-supervised learning and enabling more semantically meaningful updates at test-time. Experimental results demonstrate substantial improvements in robustness and generalization over the state-of-the-art on several benchmark datasets.