dogs-vs-cats: papers linking to it

Kaggle · www.kaggle.com/competitions/dogs-vs-cats

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to dogs-vs-cats (https://www.kaggle.com/competitions/dogs-vs-cats), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

4 papers in the tracked journals link to the Kaggle dataset dogs-vs-cats in their Crossref records, the first in 2020; most of them (2) in 2020.
By category: AI (4); the journals with the most are Journal Of Big Data (1), Applied Intelligence (1) and Neural Computing and Applications (1).

By year#

20202025

Papers linking to dogs-vs-cats by publication year
Year	Papers
2020	2
2021	1
2025	1

Journals#

Journals whose papers link to dogs-vs-cats most
Journal	Papers
Journal Of Big Data	1
Applied Intelligence	1
Neural Computing and Applications	1
Pattern Recognition	1

Papers linking to dogs-vs-cats, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-07-02	Domain randomization for neural network classification	Journal Of Big Data	Svetozar Zarko Valtchev, Jianhong Wu	29	cites it
2020-07-08	WisdomNet: trustable machine learning toward error-free classification	Neural Computing and Applications	Truong X. Tran, Ramazan Savas Aygün	17	cites it
2020-10-30	Detecting unusual input to neural networks	Applied Intelligence	Jörg Martin, Clemens Elster	6	cites it
2025-07-15	Capsule networks do not need to model everything	Pattern Recognition	Riccardo Renzulli, Enzo Tartaglione, Marco Grangetto	3	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). dogs-vs-cats: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-dogs-vs-cats

@misc{scholar4ai-code-kaggle-competition-dogs-vs-cats,
  author = {{Scholar4AI}},
  title = {dogs-vs-cats: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-dogs-vs-cats}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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