Joint Semi Supervised

Joint semi-supervised learning aims to improve model performance by leveraging both labeled and unlabeled data simultaneously across multiple related tasks. Current research focuses on developing frameworks that effectively combine information from different modalities (e.g., images and text, 2D and 3D data) or tasks (e.g., entity and relation extraction, speech recognition and synthesis), often employing graph neural networks, transformers, or convolutional neural networks to achieve this. These advancements are significant because they reduce the reliance on large labeled datasets, making machine learning more efficient and applicable to domains with limited annotated data. The resulting models often demonstrate improved accuracy and robustness compared to traditional supervised methods.