השוואת שיטות

	רשת קונבולוציה מלאה (FCN)	U-Net
תחום	למידה עמוקה	למידה עמוקה
משפחה	Machine learning	Machine learning
שנת המקור	2015	2015
הוגה השיטה	Long, J.; Shelhamer, E.; Darrell, T.	Ronneberger, O., Fischer, P., & Brox, T.
סוג	Dense pixel-wise prediction convolutional network	Encoder-decoder convolutional network with skip connections
מקור מכונן	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.
כינויים	FCN, fully convolutional network, FCN-32s, FCN-16s	U-Net, UNet, encoder-decoder with skip connections, fully convolutional segmentation network
קשורות	2	3
תקציר	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.