So sánh phương pháp

	ResNeXt	MobileNet: Mạng nơ-ron tích chập hiệu quả cho thị giác di động	ResNet (Mạng Tích chập Tái sinh)
Lĩnh vực	Học sâu	Học sâu	Học sâu
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	2017	2017	2016
Người khởi xướng	Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K.	Andrew Howard et al. (Google)	He, K.; Zhang, X.; Ren, S.; Sun, J.
Loại	Convolutional neural network with grouped/cardinality-based residual blocks	Lightweight CNN architecture	Deep Convolutional Neural Network with skip connections
Công trình gốc	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.	He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.
Tên gọi khác	ResNeXt, Aggregated Residual Transformations, grouped convolution residual network, cardinality-based ResNet	MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı	ResNet, Residual Network, Deep Residual Learning, ResNet-50
Liên quan	4	2	4
Tóm tắt	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.	ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016. By inserting shortcut (skip) connections that carry the input of a block directly to its output — defining the block's task as learning a residual correction rather than a full mapping — ResNet enabled training of networks with hundreds or even thousands of layers without the vanishing-gradient degradation that had previously made very deep networks impractical. It won the ILSVRC 2015 image recognition competition with a top-5 error of 3.57% and remains the most widely used backbone architecture in computer vision.