_Objective:_ Solve the degradation problem where adding layers induces a higher training error.

_Dataset:_ [CIFAR10](https://www.cs.toronto.edu/%7Ekriz/cifar.html), [PASCAL](http://host.robots.ox.ac.uk/pascal/VOC/) and [COCO](http://mscoco.org/).

## Inner-workings:

They argue that it is easier to learn the difference to the identity (the residual) than the actual mapping. Basically they start with the identity and learn the residual mapping.  
This allows for easier training and thus deeper network.

## Architecture:

They introduce two new building block for Residual Networks, depending on the input dimensionality:  
[![screen shot 2017-05-31 at 3 49 59 pm](https://cloud.githubusercontent.com/assets/17261080/26635061/d489dbe2-4618-11e7-911e-68772265ee9f.png)](https://cloud.githubusercontent.com/assets/17261080/26635061/d489dbe2-4618-11e7-911e-68772265ee9f.png)  
[![screen shot 2017-05-31 at 3 57 47 pm](https://cloud.githubusercontent.com/assets/17261080/26635420/f6f22af8-4619-11e7-9639-ed651f8b18bb.png)](https://cloud.githubusercontent.com/assets/17261080/26635420/f6f22af8-4619-11e7-9639-ed651f8b18bb.png)

That can then be chained to produce network such as:  
[![screen shot 2017-05-31 at 3 54 16 pm](https://cloud.githubusercontent.com/assets/17261080/26635258/7b64530c-4619-11e7-81c8-5d6be547da77.png)](https://cloud.githubusercontent.com/assets/17261080/26635258/7b64530c-4619-11e7-81c8-5d6be547da77.png)

## Results:

Won most 1st places, very impressive and adding layers do increase accuracy.