Learning the distance metric between pairs of samples has been studied for image retrieval and clustering. With the remarkable success of pair-based metric learning losses, recent works have proposed the use of generated synthetic points on metric learning losses for augmentation and generalization. However, these methods require additional generative networks along with the main network, which can lead to a larger model size, slower training speed, and harder optimization. Meanwhile, post-processing techniques, such as query expansion and database augmentation, have proposed the combination of feature points to obtain additional semantic information. In this paper, inspired by query expansion and database augmentation, we propose an augmentation method in an embedding space for pair-based metric learning losses, called embedding expansion. The proposed method generates synthetic points containing augmented information by a combination of feature points and performs hard negative pair mining to learn with the most informative feature representations. Because of its simplicity and flexibility, it can be used for existing metric learning losses without affecting model size, training speed, or optimization difficulty. Finally, the combination of embedding expansion and representative metric learning losses outperforms the state-of-the-art losses and previous sample generation methods in both image retrieval and clustering tasks. The implementation will be publicly available.

本文提出了一种在嵌入空间中进行度量学习损失的数据增强方法，即嵌入式数据增强方法，该方法结合检索扩展和数据库增强的思想，通过生成合成点来获得增强的信息。此方法与现有的度量学习损失相适应，且不影响模型大小、训练速度或优化难度，其与代表性度量学习损失相结合，在图像检索和聚类任务中优于现有的损失和样本生成方法。

嵌入扩展：嵌入空间的增强用于深度度量学习