A team of researchers is developing a new convolutional neural network for classifying images of various objects. They notice that as they add more layers to the network, the accuracy begins to stagnate or even decrease. How can the team utilize the residual learning framework to improve their model's performance?

When the network gets deeper, the signals that train the earlier layers can vanish or get corrupted, which is why accuracy stalls. Residual learning solves this by letting each block learn only the difference from its input, adding the original input back with a shortcut connection. Because the shortcut carries the gradient directly, the network can train deeper layers without losing information, and the layers can focus on refining the feature map rather than relearning identity. For example, if a block receives an image feature map \(x\) and learns a small correction \(F(x)\), the output becomes \(x + F(x)\); this keeps the original information intact while allowing the block to improve it. By inserting these residual connections throughout the CNN, the researchers can train more layers and recover higher accuracy.