Residual Networks, or ResNets, are a type of artificial neural network architecture that use skip connections to allow gradients to flow through the network without vanishing, enabling the construction of much deeper networks. This architecture addresses the degradation problem by allowing layers to learn residual mappings, which improves training efficiency and accuracy in deep learning models.