SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims at knowledge transfer from a labeled source domain to an unlabeled target domain. Most UDA techniques achieve this by reducing feature discrepancies between the two domains to learn domain-invariant feature representations. In this paper, we enhance this approach by proposing a simple yet powerful probabilistic framework (SimProF) for UDA to minimize the domain gap between the two domains. SimProF estimates the feature space distribution for each class and generates contrastive pairs by leveraging the shared categories between the source and target domains. The concept behind SimProF is inspired by the observation that normalized features in contrastive learning tend to follow a mixture of von Mises-Fisher (vMF) distributions on the unit sphere. This characteristic allows for the generation of an infinite number of contrastive pairs and facilitates an efficient optimization method using a closed-form expression for the expected contrastive loss. As a result, target semantics can be effectively used to augment source features. To implement this, we create vMF distributions based on the inter-domain feature mean difference for each class. Notably, we derive and minimize an upper bound of the expected loss, which is implicitly achieved through an estimated supervised contrastive learning loss applied to the augmented source distribution. Comprehensive experiments on cross-domain benchmarks confirm the efficacy of the proposed method.