mlsanityData to 7 Oct 2026

Very Deep Convolutional Networks for Large-Scale Image Recognition

arXiv 1409.1556 ↗First posted 4 Sep 2014cs.CVcited by 15,267

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Cited by 413 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Key facts. Cited by 15,267 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2014.

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Citing papers per year: 2018: 4; 2019: 3; 2020: 4; 2021: 3585; 2022: 3203; 2023: 2917; 2024: 2721; 2025: 1917; 2026: 913.

15,267 papers.

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