mlsanityData to 7 Oct 2026

Wide Residual Networks

arXiv 1605.07146 ↗First posted 23 May 2016cs.CV cs.LG cs.NEcited by 3,057

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Cited by 104 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at https://github.com/szagoruyko/wide-residual-networks

Key facts. Cited by 3,057 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 3; 2021: 712; 2022: 630; 2023: 671; 2024: 504; 2025: 330; 2026: 206.

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