A General Representation-Based Approach to Multi-Source Domain Adaptation

ArXi:2604.23790v1 Announce Type: new A central problem in unsupervised domain adaptation is determining what to transfer from labeled source domains to an unlabeled target domain. To handle high-dimensional observations (e.g., images), a line of approaches use deep learning to learn latent representations of the observations, which facilitate knowledge transfer in the latent space.