You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: In this paper, we address the degradation problem by introducing a deep residual learning framework.Instead of hoping each few stacked layers directly fit a desired underlying mapping, we explicitly let these layers fit a residual mapping. Formally, denoting the desired underlying mapping as \mathcal{H}(\mathbf{x}), we let the stacked nonlinear layers fit another mapping of \mathcal{F}(\mathbf{x}):=\mathcal{H}(\mathbf{x})-\mathbf{x}. The original mapping is recast into \mathcal{F}(\mathbf{x})+\mathbf{x}.We hypothesize that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.

Question: How does the deep residual learning framework address the degradation problem in deep convolutional neural networks?
The deep residual learning framework address the degradation problem using residual mapping instead of original mapping in deep convolutional neural network