Papers › EXACT: How to Train Your Accuracy

EXACT: How to Train Your Accuracy

19 May 2022arXiv:2205.09615archive 2025-07-28

Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov

Classification tasks are usually evaluated in terms of accuracy. However, accuracy is discontinuous and cannot be directly optimized using gradient ascent. Popular methods minimize cross-entropy, hinge loss, or other surrogate losses, which can lead to suboptimal results. In this paper, we propose a new optimization framework by introducing stochasticity to a model's output and optimizing expected accuracy, i.e. accuracy of the stochastic model. Extensive experiments on linear models and deep image classification show that the proposed optimization method is a powerful alternative to widely used classification losses.

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