Residual Neural Network (ResNet) - AI Research Graph - Crossminds
CrossMind.ai logo
Residual Neural Network (ResNet) - AI Research Graph
First introduced by Kaiming He, et al. (2015), ResNets is created by reformulating the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It is easier to optimize, and gains accuracy from considerably increased depth. This graph covers the important knowledge areas and research papers related to ResNet.
Top related arXiv papers
Key knowledge areas
Other recommended papers

Ishaan Gulrajani, Improved Training of Wasserstein GANs. NIPS 2017

Joseph Redmon, YOLO9000: Better, Faster, Stronger. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017

C. Ledig, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017