Label to Image

Label-to-image translation focuses on generating realistic images from semantic label maps, addressing the need for large, labeled datasets in various computer vision tasks. Current research emphasizes improving data efficiency through semi-supervised learning and leveraging unpaired data, often incorporating techniques like contrastive learning and generative adversarial networks to bridge the gap between synthetic and real-world image distributions. This field is crucial for applications requiring data augmentation, such as autonomous driving and medical image synthesis, where acquiring sufficient paired data is costly or impossible. The development of robust and efficient label-to-image models promises to significantly advance these and other data-hungry applications.