Domain Adaptation Techniques for Unsupervised Learning

Abstract

Domain adaptation is a critical task in machine learning, allowing models trained on a source domain to be effectively applied to a different target domain. Unsupervised domain adaptation (UDA) is particularly challenging as it involves adapting models without access to labeled data in the target domain. This paper provides a comprehensive analysis of domain adaptation techniques for unsupervised learning, focusing on approaches that bridge the gap between the source and target domains. We review key methodologies, such as adversarial training, discrepancy-based methods, and self-training, highlighting their strengths and limitations. Additionally, we discuss common evaluation metrics and datasets used in UDA research. Through this analysis, we aim to provide researchers and practitioners with insights into the current state of domain adaptation for unsupervised learning and avenues for future research.