Label Generation

Label generation focuses on automatically creating training labels for machine learning models, reducing the time and cost associated with manual annotation. Current research emphasizes efficient label generation techniques, often leveraging pre-trained models (like CLIP) or generative models (like diffusion models) to synthesize labels from raw data (images, point clouds, sensor data) or to create pseudo-labels through self-training. This automated labeling is crucial for advancing various fields, including image classification, object detection, and medical image analysis, by enabling the training of robust models on larger and more diverse datasets where manual labeling is impractical or impossible.