ResNet

ResNet introduced residual blocks that add a layer’s input directly to its output. These shortcut paths allow gradients to travel more easily through very deep networks.

The architecture made it possible to train networks with dozens or hundreds of layers without suffering severe performance degradation. Residual connections are now used across vision, language and multimodal systems.