How do Residual Networks (ResNet) solve the degradation problem in deep CNNs?

ResNet introduces skip connections that allow gradients and features to bypass layers, preventing performance degradation in very deep networks.

As CNNs get deeper, accuracy often saturates or degrades due to optimization difficulties. ResNet solves this using residual blocks where the input is added directly to the output of stacked layers. This creates an identity shortcut path, ensuring gradients flow more easily during backpropagation. Instead of learning full mappings, layers learn residual functions, which are easier to optimize.