পদ্ধতির তুলনা করুন

	VGGNet (Very Deep Convolutional Networks)	অ্যালেক্সনেট	রেসনেট (রেসিড্যুয়াল নেটওয়ার্ক)
ক্ষেত্র	গভীর শিখন	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2014	2012	2016
প্রবর্তক	Simonyan, K. & Zisserman, A. (Visual Geometry Group, Oxford)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	He, K.; Zhang, X.; Ren, S.; Sun, J.
ধরন	Deep Convolutional Neural Network (image classification)	Deep Convolutional Neural Network (CNN)	Deep Convolutional Neural Network with skip connections
মৌলিক উৎস	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.)	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.
অপর নাম	VGG, VGG-16, VGG-19, Very Deep ConvNet	AlexNet, Krizhevsky net, SuperVision CNN, ImageNet CNN 2012	ResNet, Residual Network, Deep Residual Learning, ResNet-50
সম্পর্কিত	4	3	4
সারসংক্ষেপ	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.	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.