融合双空间的软三元组损失，提升度量学习精度与稳定性
Combined Hyperbolic and Euclidean Soft Triple Loss Beyond the Single Space Deep Metric Learning
- 提出混合双空间代理损失：在双曲与欧氏空间中协同优化
- 在4个基准数据集上达到新最优性能，提升显著且训练更稳定
- 适合大规模数据集的度量学习，尤其适用于树状结构数据
深度度量学习（DML）旨在通过神经网络将数据映射到嵌入空间，以表征数据点间的语义相似性。双曲空间因其能更好表达树状等复杂结构而备受关注，但现有方法多依赖成对损失或无监督正则化。由于双曲空间中代理损失存在实现难题，至今未见基于代理的监督损失应用。而代理损失在大规模数据下具有更低的训练复杂度，极具吸引力。为此，本文提出联合双曲与欧氏空间的软三元组（CHEST）损失，结合双曲与欧氏空间中的代理损失及基于双曲层次聚类的正则化项。实验表明，双空间联合优化可同时提升两种空间下的度量学习准确率与训练稳定性。在四个基准数据集上验证，CHEST损失取得当前最优性能。
原文摘要 · Abstract (English)
Deep metric learning (DML) aims to learn a neural network mapping data to an embedding space, which can represent semantic similarity between data points. Hyperbolic space is attractive for DML since it can represent richer structures, such as tree structures. DML in hyperbolic space is based on pair-based loss or unsupervised regularization loss. On the other hand, supervised proxy-based losses in hyperbolic space have not been reported yet due to some issues in applying proxy-based losses in a hyperbolic space. However, proxy-based losses are attractive for large-scale datasets since they have less training complexity. To address these, this paper proposes the Combined Hyperbolic and Euclidean Soft Triple (CHEST) loss. CHEST loss is composed of the proxy-based losses in hyperbolic and Euclidean spaces and the regularization loss based on hyperbolic hierarchical clustering. We find that the combination of hyperbolic and Euclidean spaces improves DML accuracy and learning stability for both spaces. Finally, we evaluate the CHEST loss on four benchmark datasets, achieving a new state-of-the-art performance.
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