L-TT A: Lightweight Test-Time Adaptation Using a Versatile Stem Layer

Test-time adaptation (TT A) is the most realistic methodology for adapting deep learning models to the real world using only unlabeled data from the target domain. Numerous TT A studies in deep learning have aimed at minimizing entropy. However, this necessitates forward/backward processes across the entire model and is limited by the incapability to fully leverage data based solely on entropy.