Semi Supervised Training

Semi-supervised training aims to leverage both labeled and unlabeled data to train machine learning models, reducing the reliance on expensive and time-consuming data annotation. Current research focuses on improving the quality of pseudo-labels generated from unlabeled data, often employing techniques like self-training, consistency regularization, and generative models (e.g., diffusion models, VAEs) within various architectures such as transformers and graph convolutional networks. This approach is particularly impactful in domains with limited labeled data, such as medical image analysis, speech recognition, and object detection, enabling the development of more accurate and robust models with reduced annotation costs.