Weakly Supervised

Weakly supervised learning aims to train machine learning models with limited or incomplete labeled data, addressing the high cost and time associated with full annotation. Current research focuses on leveraging techniques like pseudo-labeling, self-training, and multiple instance learning, often integrated with architectures such as UNets and transformers, to achieve performance comparable to fully supervised methods across diverse applications. This approach is particularly impactful in domains with scarce labeled data, such as medical image analysis and autonomous driving, enabling the development of robust models while significantly reducing annotation effort.