Machine learning
ResNeXt
ResNeXt是由Xie、Girshick、Dollár、Tu和He于CVPR 2017年提出的一种深度卷积神经网络架构。它通过引入一个名为“基数”（cardinality）的新架构维度来扩展残差网络（ResNet）设计——即每个残差块内独立并行转换路径的数量——与之前的网络相比，它在参数更少的情况下实现了更高的准确性，并具有更简单、更统一的设计。
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来源
- Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5987–5995. DOI: 10.1109/CVPR.2017.634 ↗
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. DOI: 10.1109/CVPR.2016.90 ↗
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. ISBN: 978-0-26-203561-3
如何引用本页
ScholarGate. (2026, June 3). ResNeXt: Aggregated Residual Transformations for Deep Neural Networks. ScholarGate. https://scholargate.app/zh/deep-learning/resnext
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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- MobileNet：面向移动视觉的高效卷积神经网络深度学习↔ compare
- 残差网络（ResNet）深度学习↔ compare