Võrdle meetodeid

	ResNet (Residual Network)	DenseNet	EfficientNet
Valdkond	Süvaõpe	Süvaõpe	Süvaõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	2016	2017	2019
Looja	He, K.; Zhang, X.; Ren, S.; Sun, J.	Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.	Tan, M. & Le, Q. V.
Tüüp	Deep Convolutional Neural Network with skip connections	Dense convolutional neural network (feed-forward dense connectivity)	Compound-scaled convolutional neural network architecture
Algallikas	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.	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.	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.
Rööpnimetused	ResNet, Residual Network, Deep Residual Learning, ResNet-50	DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2
Seotud	4	2	4
Kokkuvõte	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.	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.	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.