Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time Self-Training (TeST): a technique that takes as input a model trained on some source data and a novel data distribution at test time, and learns invariant and robust representations using a student-teacher framework. We find that models adapted using TeST significantly improve over baseline test-time adaptation algorithms. TeST achieves competitive performance to modern domain adaptation algorithms, while having access to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines on two tasks: object detection and image segmentation and find that models adapted with TeST. We find that TeST sets the new state-of-the art for test-time domain adaptation algorithms.

该篇论文提出了一种Test-Time Self-Training（TeST）技术，通过使用学生-老师结构学习不变和强鲁棒性表示来适应新的分布，进而提高模型在分布变化的测试时期的适应性，其结果显示，与现代域自适应算法相比，使用TeST的模型在目标检测和图像分割上达到了最新的测试时间域适应算法的最优水平。

TeST: 分布偏移下的测试时自训练