Sammenlign metoder

	DenseNet	Inception Network (GoogLeNet)
Fagfelt	Dyp læring	Dyp læring
Familie	Machine learning	Machine learning
Opprinnelsesår	2017	2015
Opphavsperson	Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.	Christian Szegedy et al. (Google)
Type	Dense convolutional neural network (feed-forward dense connectivity)	Deep CNN with parallel multi-scale convolutions
Opprinnelig kilde	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.	Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9.
Alias	DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121	GoogLeNet, Inception v1, Deep Convolutional Neural Network (Google), Başlangıç Ağı
Relaterte	2	2
Sammendrag	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.