Test-time adaptation (TTA) allows a model to be adapted to an unseen domain without accessing the source data. Due to the nature of practical environments, TTA has a limited amount of data for adaptation. Recent TTA methods further restrict this by filtering input data for reliability, making the effective data size even smaller and limiting adaptation potential. To address this issue, We propose Feature Augmentation based Test-time Adaptation (FATA), a simple method that fully utilizes the limited amount of input data through feature augmentation. FATA employs Normalization Perturbation to augment features and adapts the model using the FATA loss, which makes the outputs of the augmented and original features similar. FATA is model-agnostic and can be seamlessly integrated into existing models without altering the model architecture. We demonstrate the effectiveness of FATA on various models and scenarios on ImageNet-C and Office-Home, validating its superiority in diverse real-world conditions.

翻译：测试时自适应（TTA）允许模型在不访问源数据的情况下适应未见过的目标域。由于实际环境的特性，TTA可用于自适应的数据量有限。近期的TTA方法为了确保可靠性，进一步通过过滤输入数据来限制数据使用，这使得有效数据规模更小，从而限制了自适应潜力。为解决此问题，我们提出了基于特征增强的测试时自适应（FATA），这是一种通过特征增强来充分利用有限输入数据的简单方法。FATA采用归一化扰动来增强特征，并使用FATA损失来适配模型，该损失使增强特征与原始特征的输出相似。FATA与模型无关，无需改变模型架构即可无缝集成到现有模型中。我们在ImageNet-C和Office-Home数据集上的多种模型和场景中验证了FATA的有效性，证明了其在多样化真实世界条件下的优越性。