Residual neural networks

tags
Neural networks
resources
(He et al. 2016)

Residual neural networks are neural networks with skip-connections (or shortcuts, residual connections) that will bypass some of the networks operations in depth.

Highway networks

(Srivastava, Greff, and Schmidhuber 2015)

DenseNets

(Huang and Liu, n.d.)

Bibliography

  1. . . "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.

  2. . n.d. “Densely Connected Convolutional Networks”, 9.

  3. . . “Highway Networks”. arXiv:1505.00387 [Cs]. http://arxiv.org/abs/1505.00387.

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