The paper "CosFace: Large Margin Cosine Loss for Deep Face Recognition" by Hao Wang et al. introduces a novel loss function called Large Margin Cosine Loss (LMCL) to enhance the discriminative power of deep features in face recognition tasks. The authors reformulate the traditional softmax loss as a cosine loss by normalizing both feature and weight vectors to remove radial variations. This normalization step, combined with a cosine margin term, maximizes the decision margin in the angular space, achieving minimum intra-class variance and maximum inter-class variance. The proposed model, referred to as CosFace, is evaluated on popular face recognition datasets such as LFW, YTF, and MegaFace, demonstrating state-of-the-art performance. The paper also provides theoretical analysis and comparisons with other loss functions, highlighting the effectiveness of LMCL in improving the discriminative power of learned features.The paper "CosFace: Large Margin Cosine Loss for Deep Face Recognition" by Hao Wang et al. introduces a novel loss function called Large Margin Cosine Loss (LMCL) to enhance the discriminative power of deep features in face recognition tasks. The authors reformulate the traditional softmax loss as a cosine loss by normalizing both feature and weight vectors to remove radial variations. This normalization step, combined with a cosine margin term, maximizes the decision margin in the angular space, achieving minimum intra-class variance and maximum inter-class variance. The proposed model, referred to as CosFace, is evaluated on popular face recognition datasets such as LFW, YTF, and MegaFace, demonstrating state-of-the-art performance. The paper also provides theoretical analysis and comparisons with other loss functions, highlighting the effectiveness of LMCL in improving the discriminative power of learned features.