Salīdzināt metodes

	ResNeXt	DenseNet
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017	2017
Autors	Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K.	Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.
Tips	Convolutional neural network with grouped/cardinality-based residual blocks	Dense convolutional neural network (feed-forward dense connectivity)
Pirmavots	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.
Citi nosaukumi	ResNeXt, Aggregated Residual Transformations, grouped convolution residual network, cardinality-based ResNet	DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121
Saistītās	4	2
Kopsavilkums	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.