Enhancing Test Time Adaptation with Few-shot Guidance

Understand

Deep neural networks often encounter significant performance drops while facing with domain shifts between training (source) and test (target) data.

• To address this issue, Test Time Adaptation (TTA) methods have been proposed to adapt pre-trained source model to handle out-of-distribution streaming target data.
• Although these methods offer some relief, they lack a reliable mechanism for domain shift correction, which can often be erratic in real-world applications.
• In response, we develop Few-Shot Test Time Adaptation (FS-TTA), a novel and practical setting that utilizes a few-shot support set on top of TTA.