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Pairwise learning boosts deep neural network data efficiency

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种成对学习框架，可显著提高深度神经网络的数据效率，尤其是在小样本回归任务中。通过将回归重新构建为聚合的相对排名，该方法在各种模型架构和数据集大小上都能提高性能，而无需额外的监督。使用这种成对方法训练的轻量级ResNet10在UTKFace数据集上的平均绝对误差（MAE）低于以往的模型，并且在STL10数据集的有限标记示例分类任务上，其性能也优于标准的交叉熵模型。 AI

影响 该方法可以使在标记数据有限的领域（如临床环境）中更有效地训练AI模型。

排序理由 该集群包含一篇详细介绍深度神经网络新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Pairwise learning boosts deep neural network data efficiency

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该集群包含一篇详细介绍深度神经网络新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Robert Bain, Marjan Rashidi, Elizabeth Chrastil ·

    Pairwise Learning Increases Data Efficiency in Deep Neural Networks

    arXiv:2610.27669v1 Announce Type: cross Abstract: Deep neural networks often fail at small-sample regression, collapsing to low-variance predictions that miss individual differences. Here, we introduce a pairwise comparison framework that reframes regression as aggregated relativ…

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