Unsupervised Domain Adaption

Unsupervised domain adaptation (UDA) tackles the challenge of applying models trained on labeled data (source domain) to unlabeled data from a different distribution (target domain). Current research focuses on leveraging pre-trained models, particularly vision-language models and convolutional neural networks, employing techniques like pseudo-labeling, contrastive learning, and optimal transport to bridge the domain gap. These advancements are significant because they reduce the reliance on extensive labeled datasets, enabling the application of machine learning to diverse real-world scenarios where labeled data is scarce or expensive to obtain.