Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun (2016)
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.
DOI: https://doi.org/10.1109/cvpr.2016.90
Abstract. Introduces ResNet and residual connections, enabling the training of networks with hundreds of layers by allowing gradients to flow directly through skip connections. A six-model ResNet ensemble won ILSVRC 2015 with 3.57% top-5 error (single-model ResNet-152 is around 4.49%); residual connections have since become a ubiquitous design pattern.
Tags: cnn resnet residual-connections