Claim · 2012 · publication · Stated in source · Supported

Krizhevsky, Sutskever and Hinton (NIPS 2012) state that a variant of their deep convolutional network was entered in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, against 26.2% for the second-best entry.

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

ImageNet classification with deep convolutional neural networks → states provision → A variant of the model entered in ILSVRC-2012 with a reported top-5 test error of 15.3%

Where it appears

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Claim and evidence

ImageNet classification with deep convolutional neural networksstates provision ↓A variant of the model entered in ILSVRC-2012 with a reported top-5 test error of 15.3%

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Provision: Statement · provision ID dl-stat-alexnet-2012
Date: 2012 · publication Records dated 2012 →

Scope and limitsThe authors' report of a competition result; the competition's own results page was not retrieved.

The evidence 01

primary sourceSupports the claim

ImageNet classification with deep convolutional neural networks

Neural Information Processing Systems · 2012

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.”

Location in source PDF p. 1, abstract

How it was read Retrieved directly from the publisher

Machine-checked · 2026-10-31

Source details and check notes

Authors: Alex Krizhevsky; Ilya Sutskever; Geoffrey E. Hinton
Version: Paper PDF in the proceedings of Advances in Neural Information Processing Systems 25 (2012). Retrieved for the candidate pack on 2026-10-28 (SHA-256 90137160c57217953d5f61857e64ca58e85f06e1b13b4f475c918b1b582b9771). The author list is the byline as printed on the first page.
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, “ImageNet classification with deep convolutional neural networks”, 2012.
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, Kinds of AI and technical history (research/technical-history). The quotation (30 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes, PDF line-break hyphens and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (31 October 2026, item 113) re-read the record’s sources and found that they support what the record states. 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. An exact framework edition, or another document with an exact expression, states one provision: a capability threshold or risk level and its trigger, a required safeguard or action, a decision authority (who decides, recommends, can overrule or is informed), a stated exception, or another statement of its content, such as a licence term or a prospectus statement. The passage records what the document says, not implementation, compliance or effectiveness. Provisions with the same key align across editions of one family.

Date. Date of the NIPS 2012 proceedings as the proceedings state it.

Record ID th-dl-stat-alexnet-2012