Towards understanding residual neural networks

Residual networks (ResNets) are now a prominent architecture in the field of deep learning. However, an explanation for their success remains elusive. The original view is that residual connections allows for the training of deeper networks, but it is not clear that added layers are always useful, or even how they are used. In this work, we find that residual connections distribute learning behavior across layers, allowing resnets to indeed effectively use deeper layers and outperform standard networks. We support this explanation with results for network gradients and representation learning that show that residual connections make the training of individual residual blocks easier.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Zeng, Brandon.

Advisor dc:contributor.advisor

• Aleksander Ma̧dry.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123067

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123067