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

According to my understanding, the "backbone" refers to the DeepLab architecture's feature extracting network. The network's input is encoded into a feature representation using this feature extractor. Around this feature extractor, the DeepLab framework "wraps" functionalities. The feature extractor can then be swapped out, and a model can be picked that best suits the task at hand in terms of accuracy, efficiency, and so on.
The phrase "backbone" in the context of DeepLab could relate to models such as ResNet, Xception, MobileNet, and so on.

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