What is a ResNet (Residual Network)?

ResNet (He et al., 2015) introduced skip connections (residual connections) that bypass one or more layers: output = F(x) + x. This solves the degradation problem — adding more layers to very deep networks was causing performance to decrease. With skip connections, gradients can flow directly through the shortcut path, enabling training of networks with 50, 100, or even 1000+ layers. ResNet won the 2015 ImageNet competition and is still widely used today as a backbone for many vision tasks. Variants: ResNet-18, ResNet-50, ResNet-101, ResNeXt, Wide ResNet.