方法对比
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| Inception Network（GoogLeNet）× | VGGNet（超深度卷积网络）× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2015 | 2014 |
| 提出者≠ | Christian Szegedy et al. (Google) | Simonyan, K. & Zisserman, A. (Visual Geometry Group, Oxford) |
| 类型≠ | Deep CNN with parallel multi-scale convolutions | Deep Convolutional Neural Network (image classification) |
| 开创性文献≠ | Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. DOI ↗ | Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs.CV]. Published at ICLR 2015. DOI ↗ |
| 别名≠ | GoogLeNet, Inception v1, Deep Convolutional Neural Network (Google), Başlangıç Ağı | VGG, VGG-16, VGG-19, Very Deep ConvNet |
| 相关≠ | 2 | 4 |
| 摘要≠ | The Inception Network, introduced by Szegedy et al. at Google in 2015 and submitted to CVPR under the name GoogLeNet, is a 22-layer deep convolutional neural network designed for large-scale image recognition. Its defining contribution is the Inception module, which applies convolutions of multiple kernel sizes in parallel and concatenates their outputs, enabling the network to capture spatial features at different scales simultaneously without a proportional increase in computational cost. | 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. |
| ScholarGate数据集 ↗ |
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