Multi Target Domain Adaptation

Multi-target Domain Adaptation (MTDA) refers to the technique of adapting a model trained on a single labeled source domain to multiple unlabeled target domains. This method aims to leverage transfer learning to reduce the reliance on annotated data in target domains, thereby enhancing the model's generalization capabilities across different environments. In the field of computer vision, MTDA can effectively address challenges in cross-domain image recognition and scene understanding, improving the practicality and robustness of models.