论文标题
kappaface：自适应添加性角度损失深脸识别
KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition
论文作者
论文摘要
特征学习是一种用于大规模面部识别的广泛使用方法。最近，大幅度损失损失方法已显示出深度识别的显着增强。这些方法提出了固定的正边缘，以实施类内部的紧凑性和阶层间多样性。但是，大多数提出的方法都不考虑阶级不平衡问题，这在开发深层识别模型的实践中是一个主要挑战。我们假设它显着影响了深面模型的概括能力。受这一观察的启发，我们引入了一种新型的自适应策略，称为Kappaface，以根据阶级的困难和失衡来调节相对重要性。在Von Mises-fisher分布的支持下，我们提出的Kappaface损失可以加强艰苦学习或低浓度类别的利润率，同时放松柜台类别。在流行的面部基准上进行的实验表明，我们提出的方法在最先进的情况下实现了卓越的性能。
Feature learning is a widely used method employed for large-scale face recognition. Recently, large-margin softmax loss methods have demonstrated significant enhancements on deep face recognition. These methods propose fixed positive margins in order to enforce intra-class compactness and inter-class diversity. However, the majority of the proposed methods do not consider the class imbalance issue, which is a major challenge in practice for developing deep face recognition models. We hypothesize that it significantly affects the generalization ability of the deep face models. Inspired by this observation, we introduce a novel adaptive strategy, called KappaFace, to modulate the relative importance based on class difficultness and imbalance. With the support of the von Mises-Fisher distribution, our proposed KappaFace loss can intensify the margin's magnitude for hard learning or low concentration classes while relaxing it for counter classes. Experiments conducted on popular facial benchmarks demonstrate that our proposed method achieves superior performance to the state-of-the-art.
