Abstract
The Data Uncertainty inherently existed in feature continuous mapping space itself and the training dataset. In this paper, a general loss function DuaFace based on Data Uncertainty and Angular/cosine-margin-based loss is proposed to study the influence of Data Uncertainty on traditional Angular based loss functions. Correspondingly, insightful analysis on how incorporating Data Uncertainty estimation helps reducing the adverse effects of noisy samples and affects the process of feature learning are also provided. Moreover, extensive experiments conducted on Face Recognition demonstrate its superiority over state-of-the-arts.
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