Generality-Training of a Classifier for Improved Calibration in Unseen Contexts

Abstract

Artificial neural networks tend to output class probabilities that are miscalibrated, i . e ., their reported uncertainty is not a very good indicator of how much we should trust the model. Consequently, methods have been developed to improve the model’s predictive uncertainty, both during training and post-hoc. Even if the model is calibrated on the domain used in training, it typically becomes over-confident when applied on slightly different target domains, e.g. due to perturbations or shifts in the data. The model can be recalibrated for a fixed list of target domains, but its performance can still be poor on unseen target domains. To address this issue, we propose a generality-training procedure that learns a modified head for the neural network to achieve better calibration generalization to new domains while retaining calibration performance on the given domains. This generality-head is trained on multiple domains using a new objective function with increased emphasis on the calibration loss compared to cross-entropy. Such training results in a more general model in the sense of not only better calibration but also better accuracy on unseen domains, as we demonstrate experimentally on multiple datasets. The code and supplementary for the paper is available ( https://github.com/bsl-traveller/CaliGen.git ).