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ResNet

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.

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Residual Network (ResNet)

Dossier de méthode taxonomique · ml-model / deep-learning

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. · DOI 10.1109/CVPR.2016.90
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv:1512.03385. · URL
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press. · ISBN 978-0-262-03561-3

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Méthodes apparentées

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Same method familyAlexNetmachine-suggested · Relational suggestion, not evidence.Same method familyDenseNetmachine-suggested · Relational suggestion, not evidence.Same method familyEfficientNetmachine-suggested · Relational suggestion, not evidence.Same method familyInception Networkmachine-suggested · Relational suggestion, not evidence.