Domain Adaptation using Self-Training with Mixup for One-Stage Object Detection

In this paper, we present an end-to-end domain adaptation technique that utilizes both feature distribution alignment and Self-Training effectively for object detection. One set of methods for domain adaptation relies on feature distribution alignment and adapts models on an unlabeled target domain by learning domain invariant representations through adversarial loss. Although this approach is effective, it may not be adequate or even have an adverse effect when domain shifts are large and inconsistent. Another set of methods utilizes Self-Training which relies on pseudo labels to approximate the target domain distribution directly. However, it can also have a negative impact on the model performance due to erroneous pseudo labels. To overcome these two issues, we propose to generate reliable pseudo labels through feature distribution alignment and data distillation. Further, to minimize the adverse effect of incorrect pseudo labels during Self-Training we employ interpolation-based consistency regularization called mixup. While distribution alignment helps in generating more accurate pseudo labels, mixup regularization of Self-Training reduces the adverse effect of less accurate pseudo labels. Both approaches supplement each other and achieve effective adaptation on the target domain which we demonstrate through extensive experiments on one-stage object detector. Experiment results show that our approach achieves a significant performance improvement on multiple benchmark datasets.