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What is Deep Residual Learning for ImageNet about?

This document presents a residual learning framework that allows neural networks to be substantially deeper, overcoming the degradation problem. The authors show that adding identity skip connections that simply add the input to the output of the stacked layers eases the training of very deep networks. Using residual learning, they were able to create networks over 8 times deeper than previous networks. An ensemble of these residual networks achieved state-of-the-art results on the ImageNet classification t

Author

Akshat Gurnani

Language

EN