Abstract
Softmax loss is commonly used to train convolutional neural networks (CNNs), but it treats all samples equally. Focal loss focus on training hard samples and takes the probability as the measurement of whether the sample is easy or hard one. In this paper, we use cosine distance of features and the corresponding centers as weight and propose weighted softmax loss (called C-Softmax). Unlike focal loss, we give greater weight to easy samples. Experiment results show that the proposed C-Softmax loss can train many well known models like ResNet, ResNeXt, DenseNet and Inception V3, and the performance of the proposed loss is better than softmax loss and focal loss.
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Zhang, H., Wang, X., He, Z. (2018). Weighted Softmax Loss for Face Recognition via Cosine Distance. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_37
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DOI: https://doi.org/10.1007/978-3-319-97909-0_37
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