A prototype-oriented clustering for domain shift with source privacy

Korawat Tanwisuth, Shujian Zhang, Pengcheng He, Mingyuan Zhou

Keywords: deep learning, clustering, privacy, computer vision

TL;DR: We propose a method to solve the problem of unsupervised clustering under domain shift and privacy concerns.

Abstract: Unsupervised clustering under domain shift (UCDS) studies how to transfer the knowledge from abundant unlabeled data from multiple source domains to learn the representation of the unlabeled data in a target domain. In this paper, we introduce Prototype-oriented Clustering with Distillation (PCD) to not only improve the performance and applicability of existing methods for UCDS, but also address the concerns on protecting the privacy of both the data and model of the source domains. PCD first constructs a source clustering model by aligning the distributions of prototypes and data. It then distills the knowledge to the target model through cluster labels provided by the source model while simultaneously clustering the target data. Finally, it refines the target model on the target domain data without guidance from the source model. Experiments across multiple benchmarks show the effectiveness and generalizability of our source-private clustering method.

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