Pairwise Loss

Pairwise loss functions in machine learning optimize models by comparing pairs of data points, aiming to learn embeddings that reflect semantic similarity or preference orderings. Current research focuses on improving efficiency (e.g., through sparse sampling or approximation techniques) and addressing limitations of existing methods, such as inconsistent optimization directions in multi-task learning scenarios or the impact of imbalanced data. These advancements are driving improvements in various applications, including recommendation systems, object re-identification, and large language model training, by enhancing model accuracy and training efficiency.