This paper presents a novel approach, called Prototype-based
Self-Distillation (ProS), for unsupervised face representation learning. The
existing supervised methods heavily rely on a large amount of annotated
training facial data, which poses challenges in terms of data collection and
privacy concerns. To address these issues, we propose ProS, which leverages a
vast collection of unlabeled face images to learn a comprehensive facial
omni-representation. In particular, ProS consists of two vision-transformers
(teacher and student models) that are trained with different augmented images
(cropping, blurring, coloring, etc.). Besides, we build a face-aware retrieval
system along with augmentations to obtain the curated images comprising
predominantly facial areas. To enhance the discrimination of learned features,
we introduce a prototype-based matching loss that aligns the similarity
distributions between features (teacher or student) and a set of learnable
prototypes. After pre-training, the teacher vision transformer serves as a
backbone for downstream tasks, including attribute estimation, expression
recognition, and landmark alignment, achieved through simple fine-tuning with
additional layers. Extensive experiments demonstrate that our method achieves
state-of-the-art performance on various tasks, both in full and few-shot
settings. Furthermore, we investigate pre-training with synthetic face images,
and ProS exhibits promising performance in this scenario as well.

该论文提出了一种名为 ProS 的新方法，用于无监督的人脸表示学习，通过利用大量未标记的人脸图像进行综合的面部全表示学习，并通过原型匹配损失来增强学到特征的区分性，进一步针对不同任务进行微调，取得了领先的性能，同时还探讨了利用合成人脸图像进行预训练的效果。