Understanding ResNet: How Deep Networks Became Trainable

Deep Residual Networks: Understanding How Very Deep Models Became Trainable Deep learning models have generally improved as they become deeper. The intuition is simple: more layers allow the model to learn more complex patterns and richer representations. However, this idea does not always work in practice. However, beyond a certain point, increasing the depth of a network starts to hurt performance instead of improving it . This observation led to the work presented in Deep Residual Learning for Image Recognition , where the authors focused on understanding why deeper networks fail and how to address this issue effectively. Why Increasing Depth Was Not Working In theory, a deeper network should perform at least as well as a shallower one. If additional layers are not useful, the network could simply learn identity mappings and behave like a smaller model. However, this does not happen in practice. As networks become deeper: Training error increases Optimization becomes more diffi...