Second Ranked Logits

Second-ranked logits, the probabilities assigned to the second-most likely class by a model, are becoming a focus in machine learning research. Current work explores their use in improving knowledge distillation, enhancing adversarial attack strategies, and addressing challenges in federated learning and long-tailed recognition by leveraging information beyond the top prediction. This research aims to improve model robustness, calibration, and efficiency, impacting areas such as model compression, out-of-distribution detection, and the security of large language models.