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.

我们提出了一种名为反崩溃损失函数的新方法，该方法通过最大化样本特征或类别代理的平均编码率来提高嵌入空间中特征聚类的稀疏性，以防止特征崩溃并促进模型的泛化性能。与现有的前沿方法相比，我们的方法在基准数据集上进行的综合实验表明，其表现优于现有的最先进方法。大量实验还验证了我们的方法在防止嵌入空间崩溃和促进泛化性能方面的有效性。

基于编码率度量的深度度量学习抗塌陷损失