Rethinking Metric Learning: Enhancing Generalization to Unseen Classes

초록

Deep Metric Learning (DML) is critical for constructing feature spaces where similar samples cluster closely, yet conventional cross-entropy (CE)-based methods often struggle to generalize to novel, unseen classes. The primary challenge arises from train-test scale misalignment, which disproportionately affects ambiguous (low-confidence) samples at test time and reduces separability. This paper presents a robust CE-based DML framework specifically designed to enhance generalization for unseen categories. We introduce Embedding Space Augmentation (ESA), a plug-in training component that (i) deliberately lowers the confidence of hard samples to amplify corrective gradients and (ii) guides updates along each class's principal eigen-direction, thereby reducing intra-class variance and restoring train-test norm alignment. We quantify alignment with scale-invariant diagnostics - the Relative Radius Ratio and mean-radius gaps - and observe that ESA maintains small gaps throughout training, whereas CE drifts over time. Comprehensive experiments on standard image retrieval benchmarks, including CUB200, CARS196, SOP, and In-Shop, validate the effectiveness of the proposed framework. Our method addresses the generalization gap, achieving superior retrieval performance and consistently yielding substantial Recall@1 gains over multiple baseline networks. The technique is modular and can be seamlessly integrated into existing CE-based DML methods, offering significant and consistent accuracy improvements.

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