Claim · 2015-12-10 · publication · Stated in source · Supported

The paper presents a residual learning framework intended to ease the training of substantially deeper neural networks.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

Deep Residual Learning for Image Recognition → states its contribution → The paper presents a residual learning framework intended to ease the training of substantially deeper neural networks.

Where it appears

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Claim and evidence

Deep Residual Learning for Image Recognitionstates its contribution ↓The paper presents a residual learning framework intended to ease the training of substantially deeper neural networks.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2015-12-10 · publication Records dated 2015 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

Deep Residual Learning for Image Recognition

arXiv · 2015-12-10

Cite the original source

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Source excerpt

“We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Kaiming He; Xiangyu Zhang; Shaoqing Ren; Jian Sun
Version: 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.
Version date: 2015-12-10
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Deep Residual Learning for Image Recognition”, 2015-12-10.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (20 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 10 December 2015 (UTC); later versions may word it differently.

Record ID kp-states-deep-residual-learning-image-recognition-2015-1