Bridging Domain Shifts Through Self-Contrastive Learning And Distribution Alignment

Deep learning models excel in computer vision tasks with large labeled datasets but often struggle with performance degradation under domain shifts to unlabeled target domains. Unsupervised Domain Adaptation (UDA) mitigates this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. In this paper, we propose Self-Contrastive Learning for Domain Adaptation (SCoDA), a novel framework that combines self-supervised contrastive learning with distributional alignment techniques to learn domain-invariant representations. SCoDA jointly optimizes domain alignment and classification by using contrastive loss to reduce feature gaps while preserving task performance through source supervision. Experiments on UDA benchmarks demonstrate that SCoDA outperforms traditional baselines, achieving accuracy improvements without relying on target labels. These results highlight SCoDA’s effectiveness in addressing complex domain shifts and its potential for real-world applications. Our code is available at https://github.com/viniavena/SCoDA.