True or False: Identity mapping in deep models necessarily implies that adding more layers will always improve the model's performance without any risk of overfitting or increased training time.

False. Identity mapping, such as the skip connections used in ResNet, helps gradients flow and can prevent degradation, but it does not guarantee that every extra layer will make the network better. Adding layers still increases the number of parameters, which can cause overfitting on small datasets and slows down training because more operations are required. For instance, a 50‑layer ResNet may outperform a 20‑layer version on ImageNet, but a 200‑layer ResNet can overfit a tiny toy dataset and take much longer to train. Thus, identity mapping eases training but does not eliminate risks of overfitting or longer training times.