Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps

Learning to reject unknown samples (not present in the source classes) in the target domain is fairly important for unsupervised domain adaptation (UDA). There exist two typical UDA scenarios, i.e., open-set, and open-partial-set, and the latter assumes that not all source classes appear in the target domain. However, most prior methods are designed for one UDA scenario and always perform badly on the other UDA scenario. Moreover, they also require the labeled source data during adaptation, limiting their usability in data privacy-sensitive applications. To address these issues, this paper proposes a Universal Model ADaptation (UMAD) framework which handles both UDA scenarios without access to the source data nor prior knowledge about the category shift between domains. Specifically, we aim to learn a source model with an elegantly designed two-head classifier and provide it to the target domain. During adaptation, we develop an informative consistency score to help distinguish unknown samples from known samples. To achieve bilateral adaptation in the target domain, we further maximize localized mutual information to align known samples with the source classifier and employ an entropic loss to push unknown samples far away from the source classification boundary, respectively. Experiments on open-set and open-partial-set UDA scenarios demonstrate that UMAD, as a unified approach without access to source data, exhibits comparable, if not superior, performance to state-of-the-art data-dependent methods.