Memory-Efficient Learned Image Compression With Pruned Hyperprior Module

The core of face recognition task is to learn the discriminative feature representation, which has intra-class compactness and inter-class separability. in recent years, some margin-based softmax loss functions were designed to encourage the intra-class compactness, but they neglect the inter-class separability. RegularFace were proposed to increase the inter-class separability. However, RegularFace is inefficient and memory-consumptive on large datasets with large numbers of identities. in this paper, we propose a novel method, named EogFace. It can encourage both the intra-compactness and the inter-class separability. EogFace has intuitive geometric interpretation and theoretical proof, which is easy to implement and only adds negligible computational overhead. Extensive experiments on popular benchmarks of face recognition showed the effectiveness of method over existing state-of-the-art(SOTA) algorithms. Our codes will be released soon.