Claim · 2019-05-28 · publication · Stated in source · Supported

The paper proposes a scaling method that uniformly scales network depth, width and resolution using what it calls a compound coefficient.

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What it connects

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks → states its contribution → The paper proposes a scaling method that uniformly scales network depth, width and resolution using what it calls a compound coefficient.

Where it appears

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Claim and evidence

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networksstates its contribution ↓The paper proposes a scaling method that uniformly scales network depth, width and resolution using what it calls a compound coefficient.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2019-05-28 · publication Records dated 2019 →

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

The evidence 01

primary sourceSupports the claim

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

arXiv · 2019-05-28

Cite the original source

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Source excerpt

“Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient.”

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

Machine-checked · 2026-10-31

Source details and check notes

Authors: Mingxing Tan; Quoc V. Le
Version: arXiv:1905.11946v1, submitted 28 May 2019 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 c74eb39a6142d448dc61c7ccb9afb1170f5a0509c0a02566f523ce56893bf80f). The author list is the byline as printed on the first page.
Version date: 2019-05-28
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Mingxing Tan, Quoc V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, 2019-05-28.
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 (25 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. A cross-family machine check (2026-10-30, item B048) found the quotation at its locator. 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 28 May 2019 (UTC); later versions may word it differently.

Record ID kp-states-efficientnet-rethinking-model-scaling-convolutional-2019-1