Claim · 2015-02-11 · publication · Stated in source · Supported

The paper names the change in each layer’s input distribution during training internal covariate shift and states that it addresses the problem by normalizing layer inputs.

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What it connects

Batch Normalization (2015 paper) → states its contribution → The paper names the change in each layer’s input distribution during training internal covariate shift and states that it addresses the problem by normalizing layer inputs.

Where it appears

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Claim and evidence

Batch Normalization (2015 paper)states its contribution ↓The paper names the change in each layer’s input distribution during training internal covariate shift and states that it addresses the problem by normalizing layer inputs.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2015-02-11 · 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

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

arXiv · 2015-02-11

Cite the original source

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Source excerpt

“We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs.”

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

Machine-checked · 2026-10-31

Source details and check notes

Authors: Sergey Ioffe; Christian Szegedy
Version: arXiv:1502.03167v1, submitted 11 February 2015 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 4591881ed6e7b3815eaa7c13cb8d8cc1c807ca5411150c11d11f718e6ba48949). The author list is the byline as printed on the first page.
Version date: 2015-02-11
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Sergey Ioffe, Christian Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”, 2015-02-11.
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 (17 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 B037) 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 11 February 2015 (UTC); later versions may word it differently.

Record ID kp-states-batch-normalization-accelerating-deep-network-2015-1