Machine learning
EfficientNet
EfficientNet 是 Mingxing Tan 和 Quoc V. Le（Google Brain）于 2019 年 ICML 上提出的一系列卷积神经网络架构。该系列通过单一的复合系数系统地联合缩放网络的深度、宽度和输入分辨率，在参数量和计算量（FLOPs）远少于 ResNet 和 Inception 等先前网络的情况下，实现了最先进的图像分类精度。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Tan, M. & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6105–6114. link ↗
- Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning. MIT Press. ISBN: 978-0-262-03561-3
如何引用本页
ScholarGate. (2026, June 3). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ScholarGate. https://scholargate.app/zh/deep-learning/efficientnet
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.
Compare side by side →