Face recognition (FR) has seen significant advancements due to the
utilization of large-scale datasets. Training deep FR models on large-scale
datasets with multiple GPUs is now a common practice. In fact, computing power
has evolved into a foundational and indispensable resource in the area of deep
learning. It is nearly impossible to train a deep FR model without holding
adequate hardware resources. Recognizing this challenge, some FR approaches
have started exploring ways to reduce the time complexity of the
fully-connected layer in FR models. Unlike other approaches, this paper
introduces a simple yet highly effective approach, Moving Haar Learning Rate
(MHLR) scheduler, for scheduling the learning rate promptly and accurately in
the training process. MHLR supports large-scale FR training with only one GPU,
which is able to accelerate the model to 1/4 of its original training time
without sacrificing more than 1% accuracy. More specifically, MHLR only needs
$30$ hours to train the model ResNet100 on the dataset WebFace12M containing
more than 12M face images with 0.6M identities. Extensive experiments validate
the efficiency and effectiveness of MHLR.

该论文提出了一种简单而非常有效的方法，Moving Haar Learning Rate（MHLR）调度器，用于在训练过程中及时准确地调整学习率，支持仅使用一块 GPU 进行大规模的人脸识别训练，能够将模型的训练时间加速到原来的 1/4，而准确度损失不超过 1%。