What ResNet introduced is adding the input of a block directly to its output, like this:

Output = 𝐹(𝑥)+ 𝑥

This academic paper introduces Deep Residual Learning, a novel framework designed to facilitate the training of exceptionally deep neural networks for image recognition. The core innovation lies in reformulating layers to learn residual functions, meaning they learn the difference from the input rather than an entirely new function. This approach effectively addresses the degradation problem, where increasing network depth paradoxically leads to higher training error, allowing for the creation of networks up to 152 layers deep, significantly outperforming shallower models. The authors demonstrate the efficacy of their Residual Networks (ResNets) across various image recognition tasks, securing first place in multiple ILSVRC and COCO 2015 competitions for classification, detection, and localization, proving the generalizability and power of their method.