Deep Cluster Distribution Alignment in Source-Target Domain Adaptation

Classification models trained on data from one source may underperform when tested on data acquired from different sources due to shifts in data distributions, which limit the models' generalizability in real-world applications. Domain adaptation methods proposed to align such shifts in sourcetarget data distributions use contrastive learning or adversarial techniques with or without internal cluster alignment. The intracluster alignment is performed using standalone $\mathbf{k}$-means clustering on image embedding. This paper introduces a novel deep clustering approach to align cluster distributions in tandem with adapting source and target data distributions. Our method learns and aligns a mixture of cluster distributions in the unlabeled target domain with those in the source domain in a unified deep representation learning framework. Experiments demonstrate that intra-cluster alignment improves classification accuracy in nine out of ten domain adaptation examples. These improvements range between 0.3 % and 2.0 % compared to k -means clustering of embedding and between 0.4 % and 5.8 % compared to methods without class-level alignment. Unlike current domain adaptation methods, the proposed cluster distribution-based deep learning provides a quantitative and explainable measure of distribution shifts in data domains. We have publicly shared the source code for the algorithm implementation.