Partial Supervision

Partial supervision in machine learning focuses on training models with incomplete or imprecisely labeled data, aiming to improve data efficiency and reduce annotation costs. Current research emphasizes developing algorithms and model architectures (including graph neural networks, transformers, and autoencoders) that effectively leverage various forms of weak supervision, such as video-level labels, noisy pairings, or partial annotations. This field is significant because it enables the application of powerful machine learning techniques to domains with limited labeled data, impacting diverse areas like medical image analysis, natural language processing, and computer vision.