新的CoCo损失函数提升了嵌入质量并加速了收敛

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-14 15:51

研究人员推出了一种新颖的损失函数CoCo，旨在增强机器学习模型中归一化和结构化表示的学习。该新目标鼓励类内坍塌和类间对比，旨在创建类之间具有显著角度分离的嵌入。在OpenML-CC18基准上的理论分析和实验表明，CoCo在类内聚类和收敛速度方面优于交叉熵和核SVM等现有方法。 AI

影响引入了一种新的损失函数，可以提高表示学习在各种机器学习任务中的效率和有效性。

排序理由该集群包含一篇详细介绍新的机器学习损失函数的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Blanca Cano-Camarero, \'Angela Fern\'andez-Pascual, Jos\'e R. Dorronsoro · 2026-07-15 04:00

Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence

arXiv:2607.12916v1 Announce Type: new Abstract: In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations. The proposed loss encourages intra-class collapse and inter-class contrast while preserving sufficient flexibility fo…
arXiv cs.LG TIER_1 English(EN) · José R. Dorronsoro · 2026-07-14 15:51

Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence

In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations. The proposed loss encourages intra-class collapse and inter-class contrast while preserving sufficient flexibility for neural networks to approximate geometrically o…