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| ResNet (Residual Network)× | Inception Network (GoogLeNet)× | |
|---|---|---|
| Campo | Apprendimento profondo | Apprendimento profondo |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2016 | 2015 |
| Ideatore≠ | He, K.; Zhang, X.; Ren, S.; Sun, J. | Christian Szegedy et al. (Google) |
| Tipo≠ | Deep Convolutional Neural Network with skip connections | Deep CNN with parallel multi-scale convolutions |
| Fonte seminale≠ | 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 ↗ | Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. DOI ↗ |
| Alias≠ | ResNet, Residual Network, Deep Residual Learning, ResNet-50 | GoogLeNet, Inception v1, Deep Convolutional Neural Network (Google), Başlangıç Ağı |
| Correlati≠ | 4 | 2 |
| Sintesi≠ | ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016. By inserting shortcut (skip) connections that carry the input of a block directly to its output — defining the block's task as learning a residual correction rather than a full mapping — ResNet enabled training of networks with hundreds or even thousands of layers without the vanishing-gradient degradation that had previously made very deep networks impractical. It won the ILSVRC 2015 image recognition competition with a top-5 error of 3.57% and remains the most widely used backbone architecture in computer vision. | 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. |
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