Positive Label

Positive label learning addresses the challenge of training machine learning models with incomplete or biased label information, focusing on scenarios where only a subset of labels (often just one positive label per data point) is available. Current research emphasizes techniques like pseudo-labeling, which leverages model predictions to create synthetically complete datasets, and addresses biases arising from non-uniform label distributions. This research is significant because it enables efficient model training with limited labeled data, impacting various applications, including multi-label image classification, temporal grounding in videos, and recommender systems, where acquiring complete annotations is costly or impractical.