Vertaile menetelmiä

	Inception-verkko (GoogLeNet)	ResNet (Residual Network)	VGGNet (Very Deep Convolutional Networks)
Tieteenala	Syväoppiminen	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning
Syntyvuosi	2015	2016	2014
Kehittäjä	Christian Szegedy et al. (Google)	He, K.; Zhang, X.; Ren, S.; Sun, J.	Simonyan, K. & Zisserman, A. (Visual Geometry Group, Oxford)
Tyyppi	Deep CNN with parallel multi-scale convolutions	Deep Convolutional Neural Network with skip connections	Deep Convolutional Neural Network (image classification)
Alkuperäislähde	Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9.	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.	Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs.CV]. Published at ICLR 2015.
Rinnakkaisnimet	GoogLeNet, Inception v1, Deep Convolutional Neural Network (Google), Başlangıç Ağı	ResNet, Residual Network, Deep Residual Learning, ResNet-50	VGG, VGG-16, VGG-19, Very Deep ConvNet
Liittyvät	2	4	4
Tiivistelmä	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.	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.	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.