Domain Transfer

Domain transfer in machine learning focuses on adapting models trained on one dataset (source domain) to perform well on a different, but related, dataset (target domain), overcoming limitations of data scarcity or domain shifts. Current research emphasizes techniques like adversarial learning, optimal transport, and meta-learning, often implemented using neural networks such as convolutional neural networks (CNNs), transformers (e.g., BERT), and generative adversarial networks (GANs), to bridge domain gaps. Successful domain transfer significantly improves the efficiency and generalizability of machine learning models, impacting diverse fields including medical image analysis, natural language processing, and robotics by enabling the reuse of existing models and data across various applications.