Semi Supervised Domain Adaptation

Semi-supervised domain adaptation (SSDA) tackles the challenge of adapting machine learning models trained on labeled source data to target domains with limited labeled examples, leveraging abundant unlabeled target data. Current research focuses on improving the utilization of scarce target labels, often employing techniques like pseudo-labeling, adversarial training, and contrastive learning within various model architectures, including those based on autoencoders, diffusion models, and graph neural networks. SSDA's significance lies in its ability to enhance model generalization across diverse data distributions, impacting fields like medical imaging, autonomous driving, and natural language processing where labeled data is expensive or difficult to obtain. This approach offers a practical solution for bridging the gap between readily available unlabeled data and the need for accurate model performance in new domains.