Deep metric learning aims to construct an embedding space where samples of the same class are close to each other, while samples of different classes are far away from each other. Most existing deep metric learning methods attempt to maximize the difference of inter-class features. And semantic related information is obtained by increasing the distance between samples of different classes in the embedding space. However, compressing all positive samples together while creating large margins between different classes unconsciously destroys the local structure between similar samples. Ignoring the intra-class variance contained in the local structure between similar samples, the embedding space obtained from training receives lower generalizability over unseen classes, which would lead to the network overfitting the training set and crashing on the test set. To address these considerations, this paper designs a self-supervised generative assisted ranking framework that provides a semi-supervised view of intra-class variance learning scheme for typical supervised deep metric learning. Specifically, this paper performs sample synthesis with different intensities and diversity for samples satisfying certain conditions to simulate the complex transformation of intra-class samples. And an intra-class ranking loss function is designed using the idea of self-supervised learning to constrain the network to maintain the intra-class distribution during the training process to capture the subtle intra-class variance. With this approach, a more realistic embedding space can be obtained in which global and local structures of samples are well preserved, thus enhancing the effectiveness of downstream tasks. Extensive experiments on four benchmarks have shown that this approach surpasses state-of-the-art methods

翻译：深度度量学习旨在构建嵌入空间，使同类样本彼此接近，而异类样本相互远离。现有深度度量学习方法大多致力于最大化类间特征的差异，通过增大嵌入空间中不同类别样本的距离来获取语义相关信息。然而，将所有正样本压缩在一起的同时在不同类别之间创造较大间隔，会无意中破坏相似样本之间的局部结构。忽略相似样本间局部结构所含的类内方差，训练得到的嵌入空间对未见类别的泛化能力较低，将导致网络过拟合训练集并在测试集上崩溃。针对这些问题，本文设计了一个自监督生成辅助排序框架，为典型的监督深度度量学习提供了一种半监督视角的类内方差学习方案。具体而言，本文对满足特定条件的样本进行不同强度和多样性的样本合成，以模拟类内样本的复杂变换；并利用自监督学习思想设计类内排序损失函数，在训练过程中约束网络保持类内分布，从而捕捉细微的类内方差。通过该方法，可获得更真实的嵌入空间，其中样本的全局与局部结构均得到良好保留，进而增强下游任务的效果。在四个基准数据集上的大量实验表明，该方法超越了现有最先进方法。