Face Representation Learning Using Composite Mini-Batches

Abstract

Mini-batch construction strategy is an important part of the deep representation learning. Different strategies have their advantages and limitations. Usually only one of them is selected to create mini-batches for training. However, in many cases their combination can be more efficient than using only one of them. In this paper, we propose Composite Mini-Batches - a technique to combine several mini-batch sampling strategies in one training process. The main idea is to compose mini-batches from several parts, and use different sampling strategy for each part. With this kind of mini-batch construction, we combine the advantages and reduce the limitations of the individual mini-batch sampling strategies. We also propose Interpolated Embeddings and Priority Class Sampling as complementary methods to improve the training of face representations. Our experiments on a challenging task of disguised face recognition confirm the advantages of the proposed methods.