विधियों की तुलना करें

	VGGNet (वेरी डीप कन्वेन्शनल नेटवर्क्स)	AlexNet	डेंसनेट	मोबाइलनेट: मोबाइल विज़न के लिए कुशल कनवल्शनल न्यूरल नेटवर्क
क्षेत्र	गहन अधिगम	गहन अधिगम	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	2014	2012	2017	2017
प्रवर्तक	Simonyan, K. & Zisserman, A. (Visual Geometry Group, Oxford)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.	Andrew Howard et al. (Google)
प्रकार	Deep Convolutional Neural Network (image classification)	Deep Convolutional Neural Network (CNN)	Dense convolutional neural network (feed-forward dense connectivity)	Lightweight CNN architecture
मौलिक स्रोत	Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs.CV]. Published at ICLR 2015.	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1097–1105. (Republished: Communications of the ACM, 60(6), 84–90, 2017.)	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.	Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint.
उपनाम	VGG, VGG-16, VGG-19, Very Deep ConvNet	AlexNet, Krizhevsky net, SuperVision CNN, ImageNet CNN 2012	DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121	MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı
संबंधित	4	3	2	2
सारांश	VGGNet is a deep convolutional neural network architecture introduced by Karen Simonyan and Andrew Zisserman at the Visual Geometry Group, Oxford, in 2014 (published at ICLR 2015). It demonstrated that network depth — achieved exclusively through stacking small 3x3 convolutional filters — is the single most critical factor for high image-classification accuracy, and its two canonical variants (VGG-16 and VGG-19) became the dominant benchmark architectures for CNN design throughout the mid-2010s.	AlexNet is a deep convolutional neural network (CNN) introduced by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) with a top-5 error rate of 15.3%, outstripping the runner-up by more than 10 percentage points and reigniting broad interest in deep learning. The architecture introduced or popularised several techniques — ReLU activations, dropout regularisation, and multi-GPU training — that became standard practice across the field.	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.	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.