Claim · 2015-03-09 · publication · Stated in source · Supported

The paper also introduces an ensemble of full models and specialist models that learn to distinguish fine-grained classes the full models confuse.

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What it connects

Distilling the Knowledge in a Neural Network → states its contribution → The paper also introduces an ensemble of full models and specialist models that learn to distinguish fine-grained classes the full models confuse.

Where it appears

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Claim and evidence

Distilling the Knowledge in a Neural Networkstates its contribution ↓The paper also introduces an ensemble of full models and specialist models that learn to distinguish fine-grained classes the full models confuse.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2015-03-09 · 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

Distilling the Knowledge in a Neural Network

arXiv · 2015-03-09

Cite the original source

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Source excerpt

“We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse.”

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

Machine-checked · 2026-10-31

Source details and check notes

Authors: Geoffrey Hinton; Oriol Vinyals; Jeff Dean
Version: arXiv:1503.02531v1, submitted 9 March 2015 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 74e6689115c539db499f9d6153edecb364059926fd45402454f8210442444c5a). The author list is the byline as printed on the first page.
Version date: 2015-03-09
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Geoffrey Hinton, Oriol Vinyals, Jeff Dean, “Distilling the Knowledge in a Neural Network”, 2015-03-09.
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 (30 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 9 March 2015 (UTC); later versions may word it differently.

Record ID kp-states-distilling-knowledge-neural-network-2015-2