no code implementations • 15 Jan 2023 • Jiayi Han, Longbin Zeng, Liang Du, Weiyang Ding, Jianfeng Feng
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.