English(EN) Learn&Drop: Fast Learning of CNNs based on Layer Dropping

Learn&Drop方法通过丢弃层将CNN训练时间减半

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-28 04:00

研究人员开发了一种名为Learn&Drop的新颖方法来加速卷积神经网络（CNN）的训练。该技术在训练过程中动态评估层参数变化，并通过丢弃未积极学习的层来缩小网络规模。与专注于推理压缩或反向传播优化的现有方法不同，Learn&Drop的目标是减少训练过程中的前向传播操作。在MNIST、CIFAR-10和Imagenette数据集上的VGG和ResNet架构上进行的实验表明，该方法可以在不显著损失准确性的情况下将训练时间缩短一半以上。 AI

影响通过减少计算开销来加速CNN训练，从而实现更快的微调和在线学习。

排序理由提出一种提高CNN训练效率新方法的学术论文。

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arXiv cs.CV TIER_1 English(EN) · Giorgio Cruciata, Luca Cruciata, Liliana Lo Presti, Jan Van Gemert, Marco La Cascia · 2026-04-28 04:00

Learn&Drop：基于层丢弃的CNN快速学习

arXiv:2604.23403v1 Announce Type: new Abstract: This paper proposes a new method to improve the training efficiency of deep convolutional neural networks. During training, the method evaluates scores to measure how much each layer's parameters change and whether the layer will co…

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Learn&Drop：基于层丢弃的CNN快速学习

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