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。
| DenseNet× | Inception Network（GoogLeNet）× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 | 2015 |
| 提出者≠ | Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q. | Christian Szegedy et al. (Google) |
| 类型≠ | Dense convolutional neural network (feed-forward dense connectivity) | Deep CNN with parallel multi-scale convolutions |
| 开创性文献≠ | Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700–4708. DOI ↗ | Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. DOI ↗ |
| 别名≠ | DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121 | GoogLeNet, Inception v1, Deep Convolutional Neural Network (Google), Başlangıç Ağı |
| 相关 | 2 | 2 |
| 摘要≠ | DenseNet (Densely Connected Convolutional Network), introduced by Huang, Liu, van der Maaten, and Weinberger at CVPR 2017 (Best Paper Award), connects every layer to every subsequent layer within a dense block so that each layer receives the concatenated feature maps of all preceding layers — maximising feature reuse, strengthening gradient flow, and achieving competitive accuracy with substantially fewer parameters than comparable architectures such as ResNet. | 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. |
| ScholarGate数据集 ↗ |
|
|