Title: Transfer Learning and Domain Adaptation: Supervised Learning Across Distribution Shifts
Authors: Virendra Tank
Volume: 10
Issue: 6
Pages: 97-104
Publication Date: 2026/06/28
Abstract:
Transfer learning and domain adaptation are two important paradigms in contemporary machine learning, allowing models to utilize knowledge from source domains to work well in target domains with only little or no labeled data. In this review, we present the theoretical framework, methodological development and practical applications of transfer learning under distribution shifts. We investigate pre-trained basis models, domain adaptation methods such as adversarial and optimal transportation approaches, the few-shot and zero-shot paradigms of learning, as well as their consideration in low-resource conditions. The paper reviews the latest progress of this fast developing technology, challenges and prospect directions are illuminated.