方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| VGGNet（超深度卷积网络）× | MobileNet：面向移动视觉的高效卷积神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014 | 2017 |
| 提出者≠ | Simonyan, K. & Zisserman, A. (Visual Geometry Group, Oxford) | Andrew Howard et al. (Google) |
| 类型≠ | Deep Convolutional Neural Network (image classification) | Lightweight CNN architecture |
| 开创性文献≠ | Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs.CV]. Published at ICLR 2015. DOI ↗ | Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint. link ↗ |
| 别名≠ | VGG, VGG-16, VGG-19, Very Deep ConvNet | MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı |
| 相关≠ | 4 | 2 |
| 摘要≠ | VGGNet is a deep convolutional neural network architecture introduced by Karen Simonyan and Andrew Zisserman at the Visual Geometry Group, Oxford, in 2014 (published at ICLR 2015). It demonstrated that network depth — achieved exclusively through stacking small 3x3 convolutional filters — is the single most critical factor for high image-classification accuracy, and its two canonical variants (VGG-16 and VGG-19) became the dominant benchmark architectures for CNN design throughout the mid-2010s. | MobileNet is a family of lightweight convolutional neural network architectures introduced by Howard et al. at Google in 2017. It is designed to run image classification, object detection, and other vision tasks directly on mobile devices and embedded systems with limited computational budgets. By replacing standard convolutions with depthwise separable convolutions and exposing two global hyperparameters, MobileNet dramatically reduces multiply-add operations and model size while retaining competitive accuracy. |
| ScholarGate数据集 ↗ |
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