Porovnať metódy

	Plne konvolučná sieť (FCN)	U-Net
Odbor	Hlboké učenie	Hlboké učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2015	2015
Tvorca	Long, J.; Shelhamer, E.; Darrell, T.	Ronneberger, O., Fischer, P., & Brox, T.
Typ	Dense pixel-wise prediction convolutional network	Encoder-decoder convolutional network with skip connections
Pôvodný zdroj	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440.	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.
Ďalšie názvy	FCN, fully convolutional network, FCN-32s, FCN-16s	U-Net, UNet, encoder-decoder with skip connections, fully convolutional segmentation network
Príbuzné	2	3
Zhrnutie	The Fully Convolutional Network (FCN), introduced by Long, Shelhamer, and Darrell at CVPR 2015, was the first end-to-end deep learning architecture trained to produce dense pixel-wise semantic segmentation maps from images of arbitrary size. By replacing the fully connected layers of a classification CNN with convolutional layers and adding learned upsampling through transposed convolutions and skip connections, FCN enabled the direct prediction of a class label for every pixel in an image, establishing the template for all subsequent segmentation architectures including U-Net and DeepLab.	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.