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新的CoCo损失函数提升了嵌入质量和训练速度

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的损失函数CoCo，旨在创建规范化且结构良好的数据表示。该函数促进类内塌陷和类间对比，使神经网络能够实现具有显著类间角度分离的几何最优嵌入。在OpenML-CC18基准上的理论分析和实验表明，CoCo相比于点回归和交叉熵等现有方法具有优势，能够带来更具信息量的梯度、更快的收敛速度和更紧密的类簇。 AI

影响 CoCo损失函数有望提高各种机器学习任务中表示学习的效率和有效性。

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学术论文。

在 arXiv cs.LG 阅读 →

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新的CoCo损失函数提升了嵌入质量和训练速度

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Blanca Cano-Camarero, \'Angela Fern\'andez-Pascual, Jos\'e R. Dorronsoro ·

    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…

  2. arXiv cs.LG TIER_1 English(EN) · José R. Dorronsoro ·

    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…

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