Salīdzināt metodes

	ResNet (Atliekošais tīkls)	U-Net
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2016	2015
Autors	He, K.; Zhang, X.; Ren, S.; Sun, J.	Ronneberger, O., Fischer, P., & Brox, T.
Tips	Deep Convolutional Neural Network with skip connections	Encoder-decoder convolutional network with skip connections
Pirmavots	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.	Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In N. Navab et al. (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, LNCS 9351 (pp. 234–241). Springer.
Citi nosaukumi	ResNet, Residual Network, Deep Residual Learning, ResNet-50	U-Net, UNet, encoder-decoder with skip connections, fully convolutional segmentation network
Saistītās	4	3
Kopsavilkums	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.	U-Net is a fully convolutional encoder-decoder architecture, introduced by Ronneberger, Fischer, and Brox at MICCAI 2015, that produces dense pixel-wise segmentation masks by combining a contracting path that captures context with a symmetric expanding path that enables precise localization — all bridged by skip connections that preserve fine spatial detail. It established the standard baseline for biomedical image segmentation and has since become one of the most widely adopted architectures for any pixel-level prediction task.