Σύγκριση μεθόδων

	ResNeXt	MobileNet: Αποδοτικά Συνελικτικά Νευρωνικά Δίκτυα για Όραση σε Κινητές Συσκευές
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2017	2017
Δημιουργός	Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K.	Andrew Howard et al. (Google)
Τύπος	Convolutional neural network with grouped/cardinality-based residual blocks	Lightweight CNN architecture
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	ResNeXt, Aggregated Residual Transformations, grouped convolution residual network, cardinality-based ResNet	MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı
Συναφείς	4	2
Σύνοψη	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.