A key factor distinguishing early convolutional networks like LeNet-5 from later, substantially deeper architectures such as ResNet is the availability of computational resources. As hardware capabilities grew—particularly through advances in GPU processing—researchers were able to design and train networks with many more layers, skip connections, and sophisticated modules. Thus, while LeNet-5 and ResNet both belong to the CNN family and share core design principles, the primary driver behind the leap in architectural complexity was the dramatic increase in accessible computation, not a fundamental change in the underlying learning paradigm.