What does backbone mean in a neural network?

Question

I am getting confused with the meaning of "backbone" in neural networks, especially in the DeepLabv3+ paper. I did some research and found out that backbone could mean

the feature extraction part of a network

DeepLabv3+ took Xception and ResNet-101 as its backbone. However, I am not familiar with the entire structure of DeepLabv3+, which part the backbone refers to, and which parts remain the same?

A generalized description or definition of backbone would also be appreciated.

Answer 1

In my understanding, the "backbone" refers to the feature extracting network which is used within the DeepLab architecture. This feature extractor is used to encode the network's input into a certain feature representation. The DeepLab framework "wraps" functionalities around this feature extractor. By doing so, the feature extractor can be exchanged and a model can be chosen to fit the task at hand in terms of accuracy, efficiency, etc.

In case of DeepLab, the term backbone might refer to models like the ResNet, Xception, MobileNet, etc.

Answer 2

TL;DR Backbone is not a universal technical term in deep learning.

(Disclaimer: yes, there may be a specific kind of method, layer, tool etc. that is called "backbone", but there is no "backbone of a neural network" in general.)

If authors use the word "backbone" as they are describing a neural network architecture, they mean

• feature extraction ( a part of the network that "sees" the input), but this interpretation is not quite universal in the field: for instance, in my opinion, computer vision researchers would use the term to mean feature extraction, whereas natural language processing researchers would not.
• in informal language, that this part in question is crucial to the overall method.