Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning
This work addresses the challenge of improving model robustness in unsupervised test-time adaptation for scenarios with distribution shifts, representing an incremental advance in TTA methods.
The paper tackles the problem of test-time adaptation (TTA) under distribution shifts by proposing a complementary learning approach that uses 'less probable categories' to reduce noise from incorrect pseudo-labels, achieving state-of-the-art performance on various datasets.
In this work, we propose a novel complementary learning approach to enhance test-time adaptation (TTA), which has been proven to exhibit good performance on testing data with distribution shifts such as corruptions. In test-time adaptation tasks, information from the source domain is typically unavailable and the model has to be optimized without supervision for test-time samples. Hence, usual methods assign labels for unannotated data with the prediction by a well-trained source model in an unsupervised learning framework. Previous studies have employed unsupervised objectives, such as the entropy of model predictions, as optimization targets to effectively learn features for test-time samples. However, the performance of the model is easily compromised by the quality of pseudo-labels, since inaccuracies in pseudo-labels introduce noise to the model. Therefore, we propose to leverage the "less probable categories" to decrease the risk of incorrect pseudo-labeling. The complementary label is introduced to designate these categories. We highlight that the risk function of complementary labels agrees with their Vanilla loss formula under the conventional true label distribution. Experiments show that the proposed learning algorithm achieves state-of-the-art performance on different datasets and experiment settings.