English(EN) Graded convolutional networks: scale invariance without the scale and robustness to unknown blur

分级卷积网络提供尺度不变性和模糊鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 05:00

研究人员开发了分级卷积网络，可在无需显式尺度归一化的情况下实现尺度不变性，并提高对未知模糊的鲁棒性。这些网络利用了对分级加权球体的收缩，该收缩分离了传统径向归一化无法处理的图像族。在MNIST数据库上的实验表明，即使在显著模糊的情况下，这些网络也能保持高精度，其性能优于依赖尺度归一化导数的网络。 AI

影响引入了一种新的图像处理架构，增强了尺度不变性和对模糊的鲁棒性，有望提高计算机视觉任务的性能。

排序理由该集群包含一篇详细介绍新型网络架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Tanush Shaska · 2026-11-09 05:00

分级卷积网络：无需尺度即可实现尺度不变性，并对未知模糊具有鲁棒性

arXiv:2611.09878v1 Announce Type: new Abstract: The Gaussian jet of an image is a graded vector space: a dilation by $\lambda$ multiplies derivatives of order $k$ by $\lambda^{-k}$. Local graded convolutions are exactly homogeneous differential operators, and a local feature is d…

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分级卷积网络：无需尺度即可实现尺度不变性，并对未知模糊具有鲁棒性

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