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Keyword: image recognition ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Deep Residual Learning for Image Recognition

   He, Zhang, Ren and Sun · arXiv (Cornell University) · 2015

   The paper introduces residual learning, which eases the training of much deeper neural networks, and reports leading results on ImageNet and COCO image recognition tasks in 2015.

   Unchecked3 claims
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   1. UncheckedThe authors report extensive experiments showing that residual networks are easier to train and gain accuracy as they become much deeper.“We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.”
   2. UncheckedAn ensemble of residual networks reached 3.57% error on the ImageNet test set, according to the paper.“An ensemble of these residual nets achieves 3.57% error on the ImageNet test set.”
   3. UncheckedThe authors say that using their very deep residual networks alone gave a 28% relative improvement on the COCO object detection dataset.“Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset.”