Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks
Training neural networks to be confident only when they're actually right
Neural networks often make confident predictions that turn out to be wrong — a problem called miscalibration. Researchers showed that you can fix this during training itself rather than afterward, by treating calibration and accuracy as linked goals solved together. The method works as well as existing techniques but requires no extra dataset, no extra parameters, and runs faster.
Miscalibrated networks are dangerous in high-stakes settings like medical diagnosis or self-driving cars, where you need to know how much to trust a prediction. Most existing fixes require training a model twice and managing extra components — overhead that multiplies in systems handling many tasks at once. This approach collapses that overhead while giving doctors, engineers, and other users reliable signals about when a model's answer is actually trustworthy.