MarginDistillation: distillation for margin-based softmax
This work addresses the need for efficient face recognition on edge devices, though it is incremental as it builds on existing margin-based softmax and distillation techniques.
The paper tackles the problem of improving face recognition performance on lightweight neural networks by proposing MarginDistillation, a novel distillation method that uses class centers from a teacher network to train the student network, achieving state-of-the-art results on LFW, AgeDB-30, and Megaface datasets.
The usage of convolutional neural networks (CNNs) in conjunction with a margin-based softmax approach demonstrates a state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a novel distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and the face embeddings, predicted by the teacher network.