  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.
