Supplementary for UniTSFace

We have derived three sample-to-sample based losses in the manuscript, i.e., USS loss, sample-to-sample based softmax, and BCE losses. The experimental evaluations of such marginal losses have been included in Sec. In our work, we choose the cosine function to represent the similarity of two features, i.e., g (x, x The learning rate starts at 0.1 and is reduced by a factor of 10 at the All models in ablation and parameter study were trained on CASIA-WebFace. For Glint360K, we train the models(ResNet-100) for 20 epochs using a batch size of 1024. The UniTSFace under the'Large' protocol of MegaFace Challenge 1 (as shown in Table 4) was trained on Glint360K.