One Shot Unsupervised Domain Adaptation

One-shot unsupervised domain adaptation (OSUDA) tackles the challenge of adapting a model trained on one domain to a new, unseen domain using only a single unlabeled image from the target domain. Current research focuses on developing efficient methods that leverage techniques like style transfer, generative models (including diffusion models), and novel data augmentation strategies to bridge the domain gap. These advancements aim to improve the performance of models in scenarios with limited target data, impacting applications such as robotics and autonomous driving where acquiring large labeled datasets is costly or impractical. The ultimate goal is to create robust and adaptable models that generalize well across diverse visual domains.