Negative Pair

Negative pair selection and generation are crucial for various machine learning tasks, aiming to improve model performance by contrasting positive examples with carefully chosen negative ones. Current research focuses on mitigating issues like false negatives (incorrectly identified negative examples) and imbalanced difficulty in negative samples, employing techniques such as adaptive denoising, weighted loss functions (e.g., Soft-InfoNCE), and dynamic bad pair mining to enhance the quality of negative pairs. These advancements are significantly impacting fields like knowledge graph embedding, code search, and contrastive learning, leading to improved model accuracy and efficiency in diverse applications.