Salīdzināt metodes

	Neirālā arhitektūras meklēšana	ResNet (Atliekošais tīkls)
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017	2016
Autors	Zoph, B. & Le, Q.V.	He, K.; Zhang, X.; Ren, S.; Sun, J.
Tips	Automated architecture optimization (deep learning)	Deep Convolutional Neural Network with skip connections
Pirmavots	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	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.
Citi nosaukumi	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	ResNet, Residual Network, Deep Residual Learning, ResNet-50
Saistītās	5	4
Kopsavilkums	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.	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.