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English(EN) Is Zero-Shot Super-Resolution Possible in Operator Learning?

算子学习的零样本超分辨率获得理论基础

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员对算子学习中的零样本超分辨率现象进行了理论研究，即在粗网格上训练的模型无需重新训练即可在更精细的网格上进行预测。研究表明，在某些良性场景下，例如秩为一的线性算子，这种能力在信息论上是不可能的。然而，该研究将输出函数的Hölder光滑性确定为成功实现零样本超分辨率的充分条件，并提供了相应的泛化界限。 AI

影响 为算子学习模型中的一项关键能力提供了理论理解。

排序理由 该集群包含一篇讨论机器学习现象理论方面的学术论文。

在 arXiv stat.ML 阅读 →

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算子学习的零样本超分辨率获得理论基础

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该集群包含一篇讨论机器学习现象理论方面的学术论文。
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Unique Subedi, Ambuj Tewari ·

    算子学习中是否存在零样本超分辨率的可能性？

    arXiv:2606.00296v1 Announce Type: new Abstract: Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate predictions on finer testing grids without additional retraining. Despite strong empi…

  2. arXiv stat.ML TIER_1 English(EN) · Ambuj Tewari ·

    算子学习中是否存在零样本超分辨率？

    Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate predictions on finer testing grids without additional retraining. Despite strong empirical evidence, the theoretical foundations of t…

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