Adversarial Domain Adaptation with Domain Mixup

Understand

Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains.

• However, two common limitations exist in current adversarial-learning-based methods.
• First, samples from two domains alone are not sufficient to ensure domain-invariance at most part of latent space.
• Second, the domain discriminator involved in these methods can only judge real or fake with the guidance of hard label, while it is more reasonable to use soft scores to evaluate the generated images or features, i.e., to fully utilize the inter-domain information.