Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs). Despite its simplicity, popularity and excellent performance, the component does not explicitly encourage discriminative learning of features. In this paper, we propose a generalized large-margin softmax (L-Softmax) loss which explicitly encourages intra-class compactness and inter-class separability between learned features. Moreover, L-Softmax not only can adjust the desired margin but also can avoid overfitting. We also show that the L-Softmax loss can be optimized by typical stochastic gradient descent. Extensive experiments on four benchmark datasets demonstrate that the deeply-learned features with L-softmax loss become more discriminative, hence significantly boosting the performance on a variety of visual classification and verification tasks.

本文提出了L-Softmax loss作为一种广义的大边际softmax（L-Softmax）损失函数, 可以显式地鼓励所学特征的类内紧密性和类间可分性, 并且能够调整想要的边际并避免过拟合, 并在四个基准数据集上进行了广泛实验, 结果表明使用L-Softmax且深度学习的特征更具有区分度, 从而大大提高了各种视觉分类和验证任务的性能。

卷积神经网络大边界Softmax损失