“…Prior domain adaptation methods first extract discriminative features on the source domain guided by source supervision. Then, they align the target features with the source features by: minimizing maximum mean discrepancy [19], minimizing maximum discrepancy of domain distributions [33,42], feature-level or pixel-level adversarial domain classifier based learning [7,14,18,36], entropy optimization [32,18,31], and finding matching pairs across domains based on optimal transport [1,4,34,38] or nearest neighbors [24,10]. Some semi-supervised learning techniques such as entropy minimization [9], pseudo-labeling [16], and Virtual Adversarial Training (VAT) [21] have been often used in domain adaptation (e.g., [17,31,44]).…”