Mixed Supervised Learning

Mixed supervised learning combines data with varying levels of annotation to train machine learning models, aiming to improve efficiency and performance compared to using only fully-labeled or unlabeled data. Current research explores this approach across diverse applications, employing various architectures like transformers, diffusion models, and ensemble methods, often focusing on optimizing training strategies and handling the complexities of integrating different data types. This technique holds significant promise for advancing fields like medical image analysis, natural language processing, and anomaly detection by enabling more efficient and robust model training with limited labeled data.