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What is Deep Residual Learning for Image Recognition about?

This document presents a residual learning framework to ease the training of substantially deeper neural networks for image recognition. The framework reformulates layers as learning residual functions rather than unreferenced functions. Empirical evidence shows residual networks are easier to optimize and can gain accuracy from increased depth. The authors evaluate residual networks up to 152 layers on ImageNet, achieving state-of-the-art results, winning 1st place in the 2015 ILSVRC competition.

Author

Vimarsh

Language

EN