Deep Residual Learning for Image Recognition.¶
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, 770-778.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Optimization Landscape
- The funnel-landscape insight from protein folding transferred into algorithm design, where shaping a loss surface to be funnel-like via normalisation and residual connections enables training of very deep networks.
This sourceResidual connections reshape the loss surface to enable training of very deep networks.
- The funnel-landscape insight from protein folding transferred into algorithm design, where shaping a loss surface to be funnel-like via normalisation and residual connections enables training of very deep networks.
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