Face representation learning using datasets with massive number of identities requires appropriate training methods. Softmax-based approach, currently the state-of-the-art in face recognition, in its usual "full softmax" form is not suitable for datasets with millions of persons. Several methods, based on the "sampled softmax" approach, were proposed to remove this limitation. These methods, however, have a set of disadvantages. One of them is a problem of "prototype obsolescence": classifier weights (prototypes) of the rarely sampled classes, receive too scarce gradients and become outdated and detached from the current encoder state, resulting in an incorrect training signals. This problem is especially serious in ultra-large-scale datasets. In this paper, we propose a novel face representation learning model called Prototype Memory, which alleviates this problem and allows training on a dataset of any size. Prototype Memory consists of the limited-size memory module for storing recent class prototypes and employs a set of algorithms to update it in appropriate way. New class prototypes are generated on the fly using exemplar embeddings in the current mini-batch. These prototypes are enqueued to the memory and used in a role of classifier weights for usual softmax classification-based training. To prevent obsolescence and keep the memory in close connection with encoder, prototypes are regularly refreshed, and oldest ones are dequeued and disposed. Prototype Memory is computationally efficient and independent of dataset size. It can be used with various loss functions, hard example mining algorithms and encoder architectures. We prove the effectiveness of the proposed model by extensive experiments on popular face recognition benchmarks.

中文翻译：

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用于大规模人脸表征学习的原型记忆

使用具有大量身份的数据集进行人脸表示学习需要适当的训练方法。基于Softmax的方法（目前最先进的人脸识别技术）通常采用“完全softmax”形式，不适用于拥有数百万人口的数据集。提出了几种基于“ sampled softmax”方法的方法来消除此限制。然而，这些方法具有一系列缺点。其中之一是“原型过时”的问题：很少采样的类的分类器权重（原型），接收的梯度太稀缺，过时和脱离当前的编码器状态，从而导致错误的训练信号。在超大规模数据集中，此问题尤其严重。在本文中，我们提出了一种新颖的人脸表征学习模型，称为原型记忆，它可以缓解此问题并允许对任意大小的数据集进行训练。原型存储器由用于存储最新类原型的有限大小的存储器模块组成，并采用一组算法以适当的方式对其进行更新。使用当前微型批处理中的示例性嵌入，可以即时生成新类的原型。这些原型排队到内存中，并以分类器权重的角色用于常规的基于softmax分类的训练。为防止过时并保持内存与编码器紧密连接，应定期刷新原型，并将最旧的原型出队并进行处置。原型存储器在计算上是有效的，并且与数据集的大小无关。它可以与各种损失函数一起使用，硬示例挖掘算法和编码器体系结构。我们通过在流行的人脸识别基准上进行广泛的实验，证明了该模型的有效性。