Sammenlign metoder

	VGGNet (Very Deep Convolutional Networks)	DenseNet
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2014	2017
Ophavsperson	Simonyan, K. & Zisserman, A. (Visual Geometry Group, Oxford)	Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.
Type	Deep Convolutional Neural Network (image classification)	Dense convolutional neural network (feed-forward dense connectivity)
Oprindelig kilde	Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs.CV]. Published at ICLR 2015.	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.
Aliasser	VGG, VGG-16, VGG-19, Very Deep ConvNet	DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121
Relaterede	4	2
Resumé	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.	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.