Wildly Unsupervised Domain Adaptation

Wildly Unsupervised Domain Adaptation (WUDA) refers to the scenario where the source domain data comes with noisy labels and the target domain data is entirely unlabeled. Through transfer learning methods, WUDA aims to effectively transfer knowledge from the source domain to the target domain to improve the performance of tasks in the target domain. This technique is designed to address the issue of distribution differences across domains and enhance the model's generalization capability in new environments, making it highly valuable for practical applications.