What is Residual Learning in ResNet architectures and why is it important?

Residual Learning introduces shortcut connections that allow neural networks to learn residual mappings instead of direct transformations, enabling extremely deep architectures to train effectively.

As neural networks become deeper, they encounter optimization difficulties such as vanishing gradients, degraded accuracy, and unstable convergence. Surprisingly, simply stacking more layers often worsens training performance instead of improving it.

Residual Learning, introduced in ResNet, solves this problem using skip connections.

Traditional layer learning: H(x)

Residual Learning reformulates this as: F(x) = H(x) - x Therefore: H(x) = F(x) + x

Instead of learning the entire mapping, the network learns only residual corrections.…