Great question! Identity layers (the skip connections at the heart of ResNet) are the clever design choice that let deep residual networks break through the "depth barrier" that stumped traditional convolutional neural networks. Let’s dive into their exact purpose and the key advantages they bring:

The main goal of identity skip connections is to solve the degradation problem in deep networks. As you add more layers to a traditional CNN, you’d expect performance to keep improving (or at least plateau) — but beyond a certain depth, performance actually drops sharply, even before overfitting sets in. This happens because forcing each layer to learn a full input-to-output mapping becomes increasingly hard as the network grows.

Identity layers fix this by letting the network learn residual mappings instead of full mappings. Instead of teaching a layer to output ( H(x) ), it only needs to learn ( F(x) = H(x) - x ) (the residual between the desired output and the input). If the optimal transformation for a layer is just to pass the input through unchanged, the network can simply set ( F(x) = 0 ) and rely on the identity connection — no need to waste parameters learning a near-identity function.

• Mitigates Vanishing/Exploding Gradients: During backpropagation, gradients can flow directly through the identity connection without passing through multiple convolutional layers. This prevents gradients from shrinking to near-zero (vanishing) or blowing up to huge values (exploding), which makes training deep networks stable and feasible.
• Enables Ultra-Deep Networks: Before ResNet, training networks deeper than ~20 layers was impractical due to degradation. With identity layers, ResNet proved you could train networks with 50, 101, even 152 layers — unlocking far more capacity to learn complex features from data.
• Simplifies Optimization: Learning residual mappings is easier than learning full input-to-output mappings. When the desired transformation is close to an identity (which is common in many layers), the residual is small, so the network has a simpler target to optimize towards.
• Preserves Information Flow: Input features (like low-level edges, textures, or colors) can bypass intermediate layers and reach deeper parts of the network directly. This ensures critical low-level information isn’t lost as data passes through multiple convolutional transformations, helping the model build richer, more accurate feature representations.
• Boosts Generalization: By giving the network the flexibility to skip unnecessary transformations, identity layers act as a form of implicit regularization. They prevent the model from overcomplicating mappings that don’t need to be complex, leading to better performance on unseen data.

To put it simply: identity layers give deep networks the option to "take a shortcut" when it’s beneficial, which makes training easier, enables deeper architectures, and ultimately leads to better performance.

内容的提问来源于stack exchange，提问作者Ali Abdari