Weakly Supervised Learning

Weakly supervised learning aims to train machine learning models using limited or imprecise labels, reducing the need for extensive, expensive manual annotation. Current research focuses on improving model performance with various techniques, including multiple instance learning (MIL), prompt tuning with vision-language models, and novel loss functions that incorporate prior knowledge or address label noise and imbalance. This field is significant because it enables the application of machine learning to diverse domains with limited labeled data, impacting areas like medical image analysis, autonomous driving, and computational chemistry.