TCP_α provides mathematically-guaranteed separation between confidence scores for correct vs. incorrect predictions, enabling reliable failure detection—rejecting just 8% of least-confident predictions improved music classification from 89% to 98% accuracy.
This paper proposes TCP_α, a new method for training neural networks to reliably estimate their own confidence in predictions. Unlike existing approaches that struggle to distinguish between correct and incorrect predictions, TCP_α uses a margin-based penalty that mathematically guarantees clear separation between confidence scores for right and wrong answers.