To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test. In this paper, we investigate a more challenging task that aims to adapt a trained CNN model to unseen domains during the test. To maximumly mine the information in the test data, we propose a unified method called DomainAdaptor for the test-time adaptation, which consists of an AdaMixBN module and a Generalized Entropy Minimization (GEM) loss. Specifically, AdaMixBN addresses the domain shift by adaptively fusing training and test statistics in the normalization layer via a dynamic mixture coefficient and a statistic transformation operation. To further enhance the adaptation ability of AdaMixBN, we design a GEM loss that extends the Entropy Minimization loss to better exploit the information in the test data. Extensive experiments show that DomainAdaptor consistently outperforms the state-of-the-art methods on four benchmarks. Furthermore, our method brings more remarkable improvement against existing methods on the few-data unseen domain. The code is available at https://github.com/koncle/DomainAdaptor.

通过 AdaMixBN 模块和广义熵最小化 (GEM) 损失函数，本论文提出了一种名为 DomainAdaptor 的统一方法，用于进行测试时的适应学习，以最大限度地挖掘测试数据中的信息，并解决训练和测试样本之间的领域偏移问题，从而在四个基准测试中稳定超过现有方法，尤其在少量数据的未知领域中带来了更显著的改进。

域适应器：一种用于测试时适应的新方法