Calibrating Deep Neural Networks by Pairwise Constraints

Jiacheng Cheng, Nuno Vasconcelos

Abstract: It is well known that deep neural networks (DNNs) pro-duce poorly calibrated estimates of class-posterior prob-abilities. We hypothesize that this is due to the limited calibration supervision provided by the cross-entropy loss, which places all emphasis on the probability of the true class and mostly ignores the remaining. We consider how each example can supervise all classes and show that the calibration of a C-way classification problem is equivalent to the calibration of C(C - 1) /2 pairwise binary classifi-cation problems that can be derived from it. This suggests the hypothesis that DNN calibration can be improved by providing calibration supervision to all such binary prob-lems. An implementation of this calibration by pairwise constraints (CPC) is then proposed, based on two types of binary calibration constraints. This is finally shown to be implementable with a very minimal increase in the complex-ity of cross-entropy training. Empirical evaluations of the proposed CPC method across multiple datasets and DNN architectures demonstrate state-of-the-art calibration per-formance.