Target Domain Image

Target domain image analysis focuses on leveraging labeled data from one domain (source) to improve performance on a related but different domain (target) where labeled data is scarce or absent. Current research emphasizes semi-supervised and unsupervised domain adaptation techniques, often employing generative adversarial networks (GANs), mean teacher models, and various consistency learning strategies to effectively utilize unlabeled target images. These methods are crucial for applications like medical image segmentation and 3D object detection where acquiring large annotated datasets is challenging, enabling more robust and efficient model training in data-limited scenarios. The resulting advancements have significant implications for various fields by improving the generalizability and applicability of deep learning models.