Adversarial Domain Adaptation

Adversarial domain adaptation (ADA) tackles the challenge of training machine learning models on data from one domain (source) and applying them effectively to another (target) with differing distributions. Current research focuses on improving ADA's performance across diverse applications, including credit risk assessment, activity recognition, and object detection, often employing adversarial networks and generative models like GANs and diffusion models, alongside vision transformers (ViTs) and pre-trained large language models (PLMs). These advancements are significant because they enable the use of readily available labeled data from one domain to improve model performance in data-scarce or differently distributed target domains, impacting fields ranging from finance to healthcare and autonomous driving. Furthermore, research explores enhancing robustness to noisy labels and improving generalization across various adversarial attacks.