Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. Prior literature predominantly focuses on pair-based and proxy-based methods to maximize inter-class discrepancy and minimize intra-class diversity. However, these methods tend to suffer from the collapse of the embedding space due to their over-reliance on label information. This leads to sub-optimal feature representation and inferior model performance. To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss. Specifically, our proposed loss primarily draws inspiration from the principle of Maximal Coding Rate Reduction. It promotes the sparseness of feature clusters in the embedding space to prevent collapse by maximizing the average coding rate of sample features or class proxies. Moreover, we integrate our proposed loss with pair-based and proxy-based methods, resulting in notable performance improvement. Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods. Extensive ablation studies verify the effectiveness of our method in preventing embedding space collapse and promoting generalization performance.

翻译：深度度量学习（DML）旨在为分类、聚类和检索等下游任务学习一个判别性的高维嵌入空间。现有文献主要关注基于样本对和基于代理的方法，以最大化类间差异并最小化类内多样性。然而，这些方法由于过度依赖标签信息，往往容易导致嵌入空间坍缩，从而产生次优的特征表示和较差的模型性能。为保持嵌入空间结构并避免特征坍缩，我们提出了一种称为抗坍缩损失的新型损失函数。具体而言，我们提出的损失主要从最大编码率缩减原理中获得启发。它通过最大化样本特征或类别代理的平均编码率，促进嵌入空间中特征簇的稀疏性，从而防止坍缩。此外，我们将所提出的损失与基于样本对和基于代理的方法相结合，实现了显著的性能提升。在基准数据集上的综合实验表明，我们提出的方法优于现有的最先进方法。广泛的消融研究验证了我们的方法在防止嵌入空间坍缩和提升泛化性能方面的有效性。