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Deep Residual Learning for Image Recognition

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Kaiming He · Shaoqing Ren

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Published 2015-12-10 authorship evidence authorship evidence

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Deep Residual Learning for Image Recognition arXiv · published 2015-12-10 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 0799899d6548fd94d79052f3b32fe5b2d1eae1aa4fb45773c5686436b2a122a3). Submission history as arXiv records it (UTC): v1 10 December 2015. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
Deep Residual Learning for Image Recognition Crossref · published 2016 · Crossref record of the conference version: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778 (DOI 10.1109/cvpr.2016.90), retrieved for the candidate pack on 2026-10-28. The date is the publisher-deposited date, kept at year precision; it was not checked against the issue.
Deep Residual Learning for Image Recognition arXiv · published 2015-12-10 · arXiv:1512.03385v1, submitted 10 December 2015 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 1e0651b6810ecba34a3dbc5b5b0209226f889004607c1f203540a48d64e5a93a). The author list is the byline as printed on the first page.

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In its own wordsThe paper presents a residual learning framework intended to ease the training of substantially deeper neural networks.“We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.” arXiv v1, PDF p. 1, abstract2015-12-10 · publication · Stated in source · Supported Inspect claim & evidence ↗