One v the Rest Loss

One-versus-the-rest (OvR) loss, a multi-class classification strategy, aims to improve model performance by training separate binary classifiers for each class against all others. Current research focuses on enhancing OvR's efficiency and robustness, particularly within object detection using ranking-based losses and in adversarial training by strategically weighting samples or switching between loss functions. These advancements address challenges like computational complexity and vulnerability to adversarial attacks, ultimately contributing to more accurate and reliable machine learning models across diverse applications.