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A Residual Neural Network (ResNet) is an Artificial Neural Network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual Neural Networks do this by utilizing skip connections, or shortcuts to jump over some layers.

Could it be used for a regression problem, e.g. housing price prediction? How about bias-variance?

Actually I am trying to train a model that predict the length of cut for operation on patients. I guess I can use FCNN to do the job. How about ResNet?

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Just to make it clear, a regression problem is one whose target is continuous and not discrete. In this sense you can make any Neural Network that is primarily used for classification a regressor, with minimal changes. Namely it needs to end with $1$ neuron, no activation function and a proper loss function (e.g. mean squared error). For example, object detection is in its core a regression problem because you are trying to predict coordinates. Any ResNet could be used for these problems.

I'm going to guess, however, that when you mean regression you mean on a structured dataset like "boston housing". This gets trickier, because here it comes down to how you define ResNet.

  • If by a ResNet architecture you mean a neural network with skip connections then yes, it can be used for any structured regression problem.

  • If you mean the specific type of CNN that is used for image classification then no. That network is build with 2D convolution layers which require their input to be 2D as well. Structured datasets won't work with this model.

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