Self Labeling

Self-labeling is a semi-supervised learning technique that leverages unlabeled data by automatically assigning pseudo-labels, thereby reducing the reliance on expensive manual annotation. Current research focuses on improving the accuracy and robustness of these pseudo-labels, often employing clustering algorithms and techniques like optimal transport to refine label assignments, particularly in challenging scenarios such as imbalanced datasets and multimodal data. This approach is significantly impacting various fields, including object detection, action localization, and novel class discovery, by enabling the training of high-performing models with limited labeled data. The resulting efficiency gains are crucial for applications where acquiring labeled data is costly or impractical.