You are designing a deep neural network to improve image recognition accuracy. Which of the following strategies would best utilize identity mapping to address common issues of training deep networks?

Using identity mapping means letting a layer simply pass its input forward unchanged, then adding it to the output of a deeper block. In practice you build residual blocks that compute a small change (the residual) and then add the original input back, which keeps gradients flowing and prevents vanishing. This trick, used in ResNet, lets you stack many layers without losing performance, because the network can learn to adjust only the differences from the identity. A small example: a 3‑layer block receives an image feature map, learns a tiny correction, and then adds the original feature map back, so the block can be deeper yet trainable. Thus, using residual connections with identity mapping is the best strategy to improve deep‑network training for image recognition.