Porovnat metody

	ResNet (reziduální síť)	AlexNet	EfficientNet	Síť Inception (GoogLeNet)
Obor	Hluboké učení	Hluboké učení	Hluboké učení	Hluboké učení
Rodina	Machine learning	Machine learning	Machine learning	Machine learning
Rok vzniku	2016	2012	2019	2015
Tvůrce	He, K.; Zhang, X.; Ren, S.; Sun, J.	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	Tan, M. & Le, Q. V.	Christian Szegedy et al. (Google)
Typ	Deep Convolutional Neural Network with skip connections	Deep Convolutional Neural Network (CNN)	Compound-scaled convolutional neural network architecture	Deep CNN with parallel multi-scale convolutions
Původní zdroj	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.	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.)	Tan, M. & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6105–6114.	Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9.
Další názvy	ResNet, Residual Network, Deep Residual Learning, ResNet-50	AlexNet, Krizhevsky net, SuperVision CNN, ImageNet CNN 2012	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2	GoogLeNet, Inception v1, Deep Convolutional Neural Network (Google), Başlangıç Ağı
Příbuzné	4	3	4	2
Shrnutí	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.	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.	EfficientNet is a family of convolutional neural network architectures introduced by Mingxing Tan and Quoc V. Le (Google Brain) at ICML 2019 that systematically co-scales network depth, width, and input resolution using a single compound coefficient, achieving state-of-the-art image classification accuracy with substantially fewer parameters and FLOPs than prior networks such as ResNet and Inception.	The Inception Network, introduced by Szegedy et al. at Google in 2015 and submitted to CVPR under the name GoogLeNet, is a 22-layer deep convolutional neural network designed for large-scale image recognition. Its defining contribution is the Inception module, which applies convolutions of multiple kernel sizes in parallel and concatenates their outputs, enabling the network to capture spatial features at different scales simultaneously without a proportional increase in computational cost.