  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.

Machine Learning;Machine Learning