Claim · 2014-09-17 · publication · Stated in source · Supported

The paper proposes a deep convolutional neural network architecture codenamed Inception, which the authors state set the best reported results for classification and detection in ILSVRC 2014.

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

Going deeper with convolutions → states its contribution → The paper proposes a deep convolutional neural network architecture codenamed Inception, which the authors state set the best reported results for classification and detection in ILSVRC 2014.

Where it appears

No collection page holds this claim. It is listed with its records in the explorer.

Claim and evidence

Going deeper with convolutionsstates its contribution ↓The paper proposes a deep convolutional neural network architecture codenamed Inception, which the authors state set the best reported results for classification and detection in ILSVRC 2014.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2014-09-17 · publication Records dated 2014 →

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

The evidence 01

primary sourceSupports the claim

Going deeper with convolutions

arXiv · 2014-09-17

Cite the original source

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

Source excerpt

“We propose a deep convolutional neural network architecture codenamed Inception, which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).”

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

Machine-checked · 2026-10-31

Source details and check notes

Authors: Christian Szegedy; Wei Liu; Yangqing Jia; Pierre Sermanet; Scott Reed; Dragomir Anguelov; Dumitru Erhan; Vincent Vanhoucke; Andrew Rabinovich
Version: arXiv:1409.4842v1, submitted 17 September 2014 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 ba83bd105ffa669ccb2aa240d3176b8cb1c98355257ddee8e64aa486d5ade92a). The author list is the byline as printed on the first page.
Version date: 2014-09-17
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, “Going deeper with convolutions”, 2014-09-17.
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 (34 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 17 September 2014 (UTC); later versions may word it differently.

Record ID kp-states-going-deeper-convolutions-2014-1