Abstract
The cosine-based softmax losses and their variants achieve great success in deep learning based face recognition. However, hyperparameter settings in these losses have significant influences on the optimization path as well as the final recognition performance. Manually tuning those hyperparameters heavily relies on user experience and requires many training tricks. In this paper, we investigate in depth the effects of two important hyperparameters of cosine-based softmax losses, the scale parameter and angular margin parameter, by analyzing how they modulate the predicted classification probability. Based on these analysis, we propose a novel cosine-based softmax loss, AdaCos, which is hyperparameter-free and leverages an adaptive scale parameter to automatically strengthen the training supervisions during the training process. We apply the proposed AdaCos loss to large-scale face verification and identification datasets, including LFW, MegaFace, and IJB-C 1:1 Verification. Our results show that training deep neural networks with the AdaCos loss is stable and able to achieve high face recognition accuracy. Our method outperforms state-of-the-art softmax losses on all the three datasets.
Abstract (translated)
基于余弦的SoftMax损失及其变体在基于深度学习的人脸识别中取得了巨大成功。然而，这些损失中的超参数设置对优化路径和最终识别性能有着重要的影响。手动调整这些超参数很大程度上依赖于用户体验，需要许多培训技巧。本文通过分析两个重要的超参数对预测分类概率的影响，深入研究了基于余弦的软最大损耗的尺度参数和角裕度参数对预测分类概率的影响。基于这些分析，我们提出了一种新的基于余弦的最大软损耗ADACOS，它不含超参数，并利用自适应尺度参数来自动加强训练过程中的训练监控。我们将所提出的ADACOS损失应用于大规模人脸验证和识别数据集，包括LFW、Megaface和IJB-C 1:1验证。结果表明，采用ADACOS损耗训练深度神经网络是稳定的，能够获得较高的人脸识别精度。我们的方法在所有三个数据集上都优于最先进的SoftMax损失。
URL
https://arxiv.org/abs/1905.00292
