Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation

Understand

Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization.

• The idea can be naturally applied to the domain adaptation task, where we can mix the source and target samples to obtain domain-mixed samples for better adaptation.
• However, the extension of the idea from classification to segmentation (i.e., structured output) is nontrivial.
• This paper systematically studies the impact of mixup under the domain adaptaive semantic segmentation task and presents a simple yet effective mixup strategy called Bidirectional Domain Mixup (BDM).