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English(EN) Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification

新的深度学习模型在无人工标注的情况下对天文瞬变体进行分类

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的深度学习框架，用于将天文瞬变体分类为真实或虚假，而无需人工标注数据。该方法利用注入的模拟瞬变体和受污染的调查数据集，采用不对称协同教学来处理不同的标签噪声水平。该框架还结合了混合不确定性量化策略，结合MC Dropout和深度集成，为其分类提供校准的置信度。该方法旨在实现时域调查中可扩展且一致的真实-虚假分类，即使在标签有噪声或有限的情况下也是如此。 AI

影响 无需人工标注数据即可实现可扩展且一致的天文瞬变体分类，提高了时域调查的效率。

排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种新的深度学习方法。

在 arXiv cs.AI 阅读 →

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新的深度学习模型在无人工标注的情况下对天文瞬变体进行分类

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该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种新的深度学习方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Rapha\"el Bonnet-Guerrini, Bruno Sanchez, Dominique Fouchez, Benjamin Racine, Maya Guy, Mariam Sabalbal, Manal Yassine, Vincenzo Piuri ·

    面向真实-虚假分类的不确定性量化，无需人工标签的可解释深度学习

    arXiv:2607.05393v1 Announce Type: cross Abstract: Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while community labels can be noisy and survey-dependent. We ai…

  2. arXiv cs.AI TIER_1 English(EN) · Vincenzo Piuri ·

    面向真实-虚假分类及不确定性量化的可解释无人工标注深度学习

    Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while community labels can be noisy and survey-dependent. We aim to develop a Real-Bogus classification framework…

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