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 image classification show that the proposed optimization method is a powerful alternative to widely used classification losses.
翻译:通常根据准确性对分类任务进行评价,但准确性不连续,不能使用梯度上升直接优化; 流行方法尽量减少交叉孔径、Hinge损失或其他代谢损失,这可能导致不理想的结果; 在本文件中,我们提出一个新的优化框架,采用模型输出的随机性,优化预期的准确性,即随机模型的准确性。 关于图像分类的广泛实验表明,拟议的优化方法是广泛使用的分类损失的有力替代方法。