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arXiv · 2205.09615

EXACT: How to Train Your Accuracy

Ivan Karpukhin·Stanislav Dereka·Sergey Kolesnikov
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Abstract

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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Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov. 2022-05-19. EXACT: How to Train Your Accuracy. https://doi.org/10.1016/j.patrec.2024.06.033

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