Sammenlign metoder

	ResNeXt	DenseNet
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2017	2017
Ophavsperson	Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K.	Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.
Type	Convolutional neural network with grouped/cardinality-based residual blocks	Dense convolutional neural network (feed-forward dense connectivity)
Oprindelig kilde	Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5987–5995.	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	ResNeXt, Aggregated Residual Transformations, grouped convolution residual network, cardinality-based ResNet	DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121
Relaterede	4	2
Resumé	ResNeXt is a deep convolutional neural network architecture introduced by Xie, Girshick, Dollár, Tu, and He at CVPR 2017. It extends the residual network (ResNet) design by introducing a new architectural dimension called cardinality — the number of independent, parallel transformation paths within each residual block — enabling higher accuracy with fewer parameters and a simpler, more uniform design than its predecessors.	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.