EXACT: How to Train Your Accuracy

Code

• github.com/tinkoff-ai/exact
  OfficialIn paperpytorch★ 10
• github.com/ivan-chai/exact
  OfficialIn paperpytorch★ 2

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.

Tasks

Benchmark Results

Reproductions