Vertaile menetelmiä

	ResNeXt	MobileNet: Tehokkaat konvoluutionaaliset neuroverkot mobiilinäköön
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2017	2017
Kehittäjä	Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K.	Andrew Howard et al. (Google)
Tyyppi	Convolutional neural network with grouped/cardinality-based residual blocks	Lightweight CNN architecture
Alkuperäislähde	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.	Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint.
Rinnakkaisnimet	ResNeXt, Aggregated Residual Transformations, grouped convolution residual network, cardinality-based ResNet	MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı
Liittyvät	4	2
Tiivistelmä	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.	MobileNet is a family of lightweight convolutional neural network architectures introduced by Howard et al. at Google in 2017. It is designed to run image classification, object detection, and other vision tasks directly on mobile devices and embedded systems with limited computational budgets. By replacing standard convolutions with depthwise separable convolutions and exposing two global hyperparameters, MobileNet dramatically reduces multiply-add operations and model size while retaining competitive accuracy.