Nasty-SFDA: Source Free Domain Adaptation from a Nasty Model

Jiajiong Cao, Yufan Liu, Weiming Bai, Jingting Ding, Liang Li

Abstract: A challenging problem called Nasty Source Free Domain Adaptation (Nasty-SFDA) is proposed in this work, where only a nasty source model and unlabeled target samples are available for DA. Further, after DA, the target model is expected to be a nasty model. In order to deal with Nasty-SFDA, Nasty HypOthesis Transfer (NHOT) with an improved version of Information Maximization (IM) loss called Multi-Peak Constraints (MPC) and several Label Generation (LG) techniques is proposed. Experiments on four popular datasets show the superiority of NHOT for both Nasty-SFDA and SFDA. In addition, the target model obtained via NHOT is proven to be a nasty model.