Residual neural networks are neural networks with skip-connections (or shortcuts, residual connections) that will bypass some of the networks operations in depth.
- He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. June 2016. "Deep Residual Learning for Image Recognition". In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–78. Las Vegas, NV, USA: IEEE. DOI.
- Huang, Gao, and Zhuang Liu. n.d. “Densely Connected Convolutional Networks”, 9.
- Srivastava, Rupesh Kumar, Klaus Greff, and Jürgen Schmidhuber. November 3, 2015. “Highway Networks”. arXiv:1505.00387 [Cs]. http://arxiv.org/abs/1505.00387.