Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation

Understand

In the classic setting of unsupervised domain adaptation (UDA), the labeled source data are available in the training phase.

• However, in many real-world scenarios, owing to some reasons such as privacy protection and information security, the source data is inaccessible, and only a model trained on the source domain is available.
• This paper proposes a novel deep clustering method for this challenging task.
• Aiming at the dynamical clustering at feature-level, we introduce extra constraints hidden in the geometric structure between data to assist the process.