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One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large- scale face recognition is the design of appropriate loss func- tions that enhance discriminative power. Centre loss pe- nalises the distance between the deep features and their cor- responding class centres in the Euclidean space to achieve intra-class compactness. SphereFace assumes that the lin- ear transformation matrix in the last fully connected layer can be used as a representation of the class centres in an angular space and penalises the angles between the deep features and their corresponding weights in a multiplicative way.

为了便于说明汇编语言指令，首先定义一些符号和缩写，这些符号和缩写都将在指令 中使用。表1 列出C54xx 系列DSP 指令系统中所使用的符号和缩写及其意义。表2 详细列 出C54xx 系列DSP 的指令系统中所使用的一些特殊符号和缩写及其意义。

数字图像处The diameter, x, of the retinal image corresponding to the dot is obtained from similar triangles, as shown in Fig. P2.1. That is, (d=2) 0:2 = (x=2) 0:014 which gives x = 0:07d. From the discussion in Section 2.1.1, and taking some liberties of interpretation, we can think of the fovea as a square sensor array having on the order of 337,000 elements, which translates into an array of size 580£ 580 elements. Assuming equal spacing between elements, this gives 580 elements and 579 spaces on a line 1.5 mm long. The size of each element and each space is then s = [(1:5mm)=1; 159] = 1:3£10¡6 m. If the size (on the fovea) of the imaged dot is less than the size of a single resolution element, we assume that the dot will be invisible to the eye. In other words, the eye will not detect a dot if its diameter, d, is such that 0:07(d) < 1:3 £ 10¡6 m, or d < 18:6 £ 10¡6 m. Figure理