Model adaptation via credible local context representation

Abstract

AbstractConventional model transfer techniques, requiring the labelled source data, are not applicable in the privacy‐protected medical fields. For the challenging scenarios, recent source data‐free domain adaptation (SFDA) has become a mainstream solution but losing focus on the inter‐sample class information. This paper proposes a new Credible Local Context Representation approach for SFDA. Our main idea is to exploit the credible local context for more discriminative representation. Specifically, we enhance the source model's discrimination by information regulating. To capture the context, a discovery method is developed that performs fixed steps walking in deep space and takes the credible features in this path as the context. In the epoch‐wise adaptation, deep clustering‐like training is conducted with two major updates. First, the context for all target data is constructed and then the context‐fused pseudo‐labels providing semantic guidance are generated. Second, for each target data, a weighting fusion on its context forms the anchored neighbourhood structure; thus, the deep clustering is switched from individual‐based to coarse‐grained. Also, a new regularisation building is developed on the anchored neighbourhood to drive the deep coarse‐grained learning. Experiments on three benchmarks indicate that the proposed method can achieve state‐of‐the‐art results.