ResNet - Literature Review

This video is an introduction to ResNet, an architecture that allows us to train extremely deep neural networks. This literature review is based on "Deep Residual Learning for Image Recognition" paper published in 2016.

OUTLINE:
0:00​ - Introduction
0:15​ - Motivation
1:46 - Related Work
3:50​ - Methodology
6:55 - Result
11:20 - Conclusion


Abstract:
Deeper neural networks are more difficult to train. We
present a residual learning framework to ease the training
of networks that are substantially deeper than those used
previously. We explicitly reformulate the layers as learning
residual functions with reference to the layer inputs, instead
of learning unreferenced functions. We provide comprehensive
empirical evidence showing that these residual
networks are easier to optimize, and can gain accuracy from
considerably increased depth. On the ImageNet dataset we
evaluate residual nets with a depth of up to 152 layers—8×
deeper than VGG nets [40] but still having lower complexity.
An ensemble of these residual nets achieves 3.57% error
on the ImageNet test set. This result won the 1st place on the
ILSVRC 2015 classification task. We also present analysis
on CIFAR-10 with 100 and 1000 layers.
The depth of representations is of central importance
for many visual recognition tasks. Solely due to our extremely
deep representations, we obtain a 28% relative improvement
on the COCO object detection dataset. Deep
residual nets are foundations of our submissions to ILSVRC
& COCO 2015 competitions1, where we also won the 1st
places on the tasks of ImageNet detection, ImageNet localization,
COCO detection, and COCO segmentation.

Authors: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun