Source data‐free domain adaptation of object detector through domain‐specific perturbation

Abstract

The current unsupervised cross-domain detection methods need source domain data to retrain the detection model in target domain. However, the source domain data may be unavailable due to privacy, decentralization, or computation resource restrictions. A natural idea is to optimize the parameters of the source domain model by self-supervised learning based on pseudo labels. We propose another approach from the viewpoint of noise perturbation without pseudo-labeling. It can be assumed that the source and target domains are actually derived from a domain invariant space through domain-specific perturbations, respectively. A super target domain can be constructed by augmenting more target domain perturbations to the target domain images. The optimal direction of the target domain to the domain invariant space can be approximated as the alignment direction from the super target domain to the target domain. Based on this idea, we propose a novel method called SOAP (SOurce data-free domain Adaptation through domain Perturbation) which can remove domain perturbation from the target domain. The image-level, instance-level, and category consistency regularizations based on Mean Teacher structure are proposed to learn the correct alignment direction. Specifically, the category consistency can also further improve the classification accuracy. Extensive experiments on multiple domain adaptation scenarios demonstrate that SOAP achieves better performance surpassing the baseline (Faster R-CNN) and multiple state-of-the-art domain adaptation methods which need to access source domain data.