The publication of Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun at IEEE Conference on Computer Vision and Pattern Recognition 2016 constituted a decisive epistemic rupture in the evolution of deep learning. Their central innovation—the formulation of residual learning through identity-based skip connections—addressed the degradation problem that had hitherto constrained the effective training of very deep convolutional neural networks. Rather than compelling stacked layers to approximate a direct mapping, the architecture reparameterised the objective as the learning of a residual function, succinctly expressed as F(x) + x, thereby stabilising gradient propagation and enabling networks exceeding one hundred layers to converge reliably. Empirically validated on ImageNet, the proposed ResNet achieved unprecedented accuracy while exhibiting remarkable optimisation efficiency, rapidly becoming a foundational scaffold for subsequent architectures across vision, language, and multimodal systems. A compelling case study of its intellectual diffusion is observable in applied domains such as Asian food classification, where hybridised ResNet variants incorporating attention mechanisms and data augmentation strategies demonstrably elevate top-1 accuracy. Thus, the ResNet paradigm exemplifies how a mathematically economical reformulation can precipitate a paradigmatic transformation, rendering it one of the most cited and structurally generative contributions in contemporary artificial intelligence scholarship. He, K., Zhang, X., Ren, S. and Sun, J. (2016) ‘Deep Residual Learning for Image Recognition’, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27–30 June, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90
