In the context of deep learning, identity mapping is primarily used to maintain the ________ from shallower models, aiding in the training of deeper networks.

Identity mapping is primarily used to maintain the information from shallower models, aiding in the training of deeper networks. By passing the input unchanged through shortcut connections, it preserves the original signal and prevents it from being lost or distorted as it passes through many layers. This keeps the learned features stable so later layers can refine them instead of relearning the same content. For example, in a ResNet, the shortcut lets the network learn only the residual change while keeping the original image features intact.