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Metric learning seeks perceptual embeddings where visually similar instancesare close and dissimilar instances are apart, but learned representations canbe sub-optimal when the distribution of intra-class samples is diverse anddistinct sub-clusters are present. Although theoretically with optimalassumptions, margin-based losses such as the triplet loss and margin loss havea diverse family of solutions. We theoretically prove and empirically show thatunder reasonable noise assumptions, margin-based losses tend to project allsamples of a class with various modes onto a single point in the embeddingspace, resulting in a class collapse that usually renders the space ill-sortedfor classification or retrieval. To address this problem, we propose a simplemodification to the embedding losses such that each sample selects its nearestsame-class counterpart in a batch as the positive element in the tuple. Thisallows for the presence of multiple sub-clusters within each class. Theadaptation can be integrated into a wide range of metric learning losses. Theproposed sampling method demonstrates clear benefits on various fine-grainedimage retrieval datasets over a variety of existing losses; qualitativeretrieval results show that samples with similar visual patterns are indeedcloser in the embedding space.

Rethinking preventing class-collapsing in metric learning with margin-based losses