Traditional deep neural networks face significant challenges when we try to make them deeper. The first problem is vanishing or exploding gradients, where the gradient signal becomes too weak or too strong as it propagates backward through many layers. The second problem is degradation, where adding more layers actually makes the network perform worse, not better. This seems counterintuitive, but it's a real limitation that prevented researchers from building very deep networks effectively.
Residual Networks solve these problems with a brilliant innovation called skip connections or residual connections. Instead of forcing each layer to learn the complete desired mapping H of x, ResNet allows layers to learn just the residual function F of x, where F of x equals H of x minus x. The final output becomes F of x plus x. This skip connection allows the gradient to flow directly through the shortcut path, preventing the vanishing gradient problem and making it much easier to train very deep networks.
Let's examine the structure of a basic residual block in detail. The input x flows through a series of layers, typically convolution followed by ReLU activation, then another convolution. This produces the transformation F of x. Meanwhile, the original input x bypasses these layers through the skip connection. At the addition node, we combine F of x with the original x to get the final output. The key insight is that if the identity mapping is optimal for this block, the layers only need to learn F of x equals zero, which is much easier than learning the complete identity transformation.
ResNet's breakthrough was enabling the training of extremely deep networks. ResNet architectures commonly have 50, 101, or even 152 layers, which was previously impossible with traditional networks. The key is stacking multiple residual blocks together, each with its own skip connections. Additionally, longer skip connections can span multiple blocks, creating highways for gradient flow throughout the entire network. Each residual block learns incremental refinements to the representation, building up complex features layer by layer while maintaining trainability through the skip connections.
ResNet's impact on deep learning cannot be overstated. It won the ImageNet competition in 2015 and enabled researchers to train networks with over 1000 layers successfully. ResNet became the foundation for many modern architectures, and skip connections are now a standard component in most deep learning models. The applications are vast, spanning image classification, object detection, medical imaging, autonomous vehicles, security systems, and robotics. ResNet proved that with the right architectural innovations, we can build much deeper and more powerful neural networks, opening the door to the current era of deep learning breakthroughs.