Domain Adaptation Techniques in Deep Learning: Exploring domain adaptation techniques to improve the generalization of deep learning models when applied to new domains

Abstract

Deep learning has shown remarkable success in various domains, but its performance often degrades when models trained on one domain are applied to a different domain. Domain adaptation techniques aim to mitigate this issue by transferring knowledge from a source domain where labeled data is abundant to a target domain with limited labeled data. This paper provides a comprehensive overview of domain adaptation techniques in deep learning, focusing on methods that improve the generalization of models across different domains. We categorize these techniques into three main approaches: feature-based, model-based, and adversarial-based methods. We discuss the underlying principles, advantages, and limitations of each approach, highlighting recent advancements and applications. Furthermore, we present key challenges and future research directions in domain adaptation for deep learning.