विधियों की तुलना करें

	पूर्णतः संवादात्मक नेटवर्क (FCN)	मास्क आर-सीएनएन: पिक्सेल-स्तरीय मास्क के साथ इंस्टेंस सेगमेंटेशन	रेसिडुअल नेटवर्क (ResNet)
क्षेत्र	गहन अधिगम	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	2015	2017	2016
प्रवर्तक	Long, J.; Shelhamer, E.; Darrell, T.	Kaiming He et al. (FAIR)	He, K.; Zhang, X.; Ren, S.; Sun, J.
प्रकार	Dense pixel-wise prediction convolutional network	Instance segmentation deep neural network	Deep Convolutional Neural Network with skip connections
मौलिक स्रोत	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.	He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), 2980–2988.	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.
उपनाम	FCN, fully convolutional network, FCN-32s, FCN-16s	Mask Region-based Convolutional Neural Network, Instance Segmentation R-CNN, He et al. 2017 Segmentation Model, Maske R-CNN	ResNet, Residual Network, Deep Residual Learning, ResNet-50
संबंधित	2	2	4
सारांश	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.	Mask R-CNN is a deep learning framework for instance segmentation introduced by Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick at Facebook AI Research (FAIR) in 2017. It extends Faster R-CNN by adding a parallel branch that predicts a binary pixel-level mask for each detected object instance, enabling simultaneous object detection, classification, and fine-grained segmentation in a single forward pass.	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.