Confidence Aware Contrastive

Confidence-aware contrastive learning aims to improve the robustness and reliability of machine learning models by incorporating confidence estimates into the contrastive learning framework. Current research focuses on enhancing model performance in scenarios with noisy labels, long-tailed data distributions, and the need for selective classification, often employing techniques like weighted averaging of features and confidence-based sample selection within contrastive loss functions. This approach is proving valuable across diverse applications, including image classification, knowledge graph error detection, and dense retrieval, by enabling more accurate and reliable predictions, especially in challenging data conditions.