Claim · 2018-01-11 · publication · Stated in source · Supported

The paper introduces a Full Reference Image Quality Assessment (FR-IQA) dataset of human perceptual judgments, which the authors describe as orders of magnitude larger than previous datasets.

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What it connects

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric → states its contribution → The paper introduces a Full Reference Image Quality Assessment (FR-IQA) dataset of human perceptual judgments, which the authors describe as orders of magnitude larger than previous datasets.

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Claim and evidence

The Unreasonable Effectiveness of Deep Features as a Perceptual Metricstates its contribution ↓The paper introduces a Full Reference Image Quality Assessment (FR-IQA) dataset of human perceptual judgments, which the authors describe as orders of magnitude larger than previous datasets.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2018-01-11 · publication Records dated 2018 →

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

The evidence 01

primary sourceSupports the claim

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

arXiv · 2018-01-11

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Source excerpt

“To answer these questions, we introduce a new Full Reference Image Quality Assessment (FR-IQA) dataset of perceptual human judgments, orders of magnitude larger than previous datasets.”

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

Machine-checked · 2026-10-31

Source details and check notes

Authors: Richard Zhang; Phillip Isola; Alexei A. Efros; Eli Shechtman; Oliver Wang
Version: arXiv:1801.03924v1, submitted 11 January 2018 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 ebab1bf040c74fd9ba2d632f714bd8eae93a93fce32c80c17e39e9cd0226b5c7). The author list is the byline as printed on the first page.
Version date: 2018-01-11
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang, “The Unreasonable Effectiveness of Deep Features as a Perceptual Metric”, 2018-01-11.
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 (26 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 11 January 2018 (UTC); later versions may word it differently.

Record ID kp-states-unreasonable-effectiveness-deep-features-as-2018-1