Gradual Domain Adaptation

Gradual domain adaptation (GDA) addresses the challenge of adapting machine learning models to new, significantly different data distributions by incrementally bridging the gap between a source (well-labeled) and target (unlabeled) domain through intermediate domains. Current research focuses on developing efficient algorithms, such as gradual self-training and generative methods like optimal transport, to navigate these intermediate steps effectively, often incorporating neural networks for feature extraction and representation learning. This approach improves model robustness and generalization, particularly valuable in applications where data distribution shifts are common, such as in computer vision, natural language processing, and drug discovery.