EXACT: How to Train Your Accuracy
Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov

TL;DR
This paper introduces a novel optimization framework that directly maximizes expected accuracy by incorporating stochasticity into model outputs, offering a promising alternative to traditional surrogate loss functions in classification tasks.
Contribution
It presents a new method for directly optimizing accuracy through stochastic output models, addressing limitations of existing surrogate loss approaches.
Findings
Outperforms traditional loss functions in experiments
Effective for both linear and deep models
Provides a new perspective on accuracy optimization
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning and Data Classification · Neural Networks and Applications · Face and Expression Recognition
