English(EN) Hybrid Dual-Batch and Cyclic Progressive Learning for Efficient Distributed Training of Convolutional Neural Networks

混合学习方法提高了CNN训练效率和准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了一种新颖的混合方法来加速卷积神经网络（CNN）的分布式训练。该方法结合了双批次学习方案（使用大和小批次大小来提高效率和泛化能力）和循环渐进学习方案（在训练过程中逐渐增加图像分辨率）。实验表明，这种混合技术可以显著减少训练时间，同时提高模型在CIFAR-100和ImageNet等数据集上的准确性。 AI

影响这种混合训练方法可以通过降低计算成本，从而加快计算机视觉模型的开发周期。

排序理由这是一篇详细介绍神经网络新训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Kuan-Wei Lu, Ding-Yong Hong, Pangfeng Liu, Jan-Jan Wu · 2026-11-06 05:00

用于卷积神经网络高效分布式训练的混合双批次和循环渐进学习

arXiv:2509.26092v3 Announce Type: replace-cross Abstract: Distributed deep learning is critical for training convolutional neural network (CNN) models on large-scale image datasets with numerous parameters. Current research primarily focuses on leveraging additional hardware reso…

本文如何被排名

报道来源 [1]

用于卷积神经网络高效分布式训练的混合双批次和循环渐进学习

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