Semi Supervised Meta

Semi-supervised meta-learning aims to improve the efficiency of few-shot learning by leveraging both labeled and unlabeled data during the meta-training phase. Current research focuses on developing algorithms that effectively incorporate unlabeled data, often employing techniques like pseudo-labeling and contrastive learning, within established meta-learning frameworks such as Model-Agnostic Meta-Learning (MAML) and variations thereof. This approach addresses the limitations of traditional meta-learning, which requires substantial labeled data, making it particularly relevant for applications with limited annotated information, such as brain-computer interfaces and spatiotemporal data analysis. The resulting models demonstrate improved performance on tasks with scarce labeled examples across diverse domains.