Source Free Domain Adaptation via Adapting to the Enhanced Style

Unsupervised domain adaptation (UDA) assumes a labeled source domain and an unlabeled target domain. It aims to train a target domain model by transferring the knowledge from source domain to target domain. With the same goal, source-free domain adaptation (SFDA) only uses the trained source model and target domain data for target model training, which is beneficial for data transmission and data privacy protection etc. Existing SFDA methods suffer from domain shift, resulting in unsatisfactory results. Different from existing methods, we eliminate the domain shift from the domain style perspective. Specifically, we propose a novel method named Adapting to the Enhanced Style (AES). We first increase the diversity of target domain style by style enhancement, then a contrastive loss is used to adapt to the diverse generated styles by requiring consistent feature representations. This process forces our classification model adapting to target domain style, thus increasing the robustness of the model. We conduct extensive experiments on standard benchmarks, and the results show the superiority of our method.