LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Training of video diffusion models is often augmented with readily available data sets of labeled images.
“For that reason, training is often augmented with readily available data sets of labeled images (e.g. Russakovsky et al. 2015, Schuhmann et al. 2022, see Section 6.2).”
From Video Diffusion Models: A Survey (2024) · cites this paper as Schuhmann et al. 2022
Current data collection processes prioritize scale over inclusion, relying on web-scraping and synthetic pipelines that reflect developer biases and the uneven availability of online content.
“Current data collection processes prioritize scale over inclusion, relying on web-scraping (e.g., [40, 97]) and synthetic pipelines that reflect developer biases and the uneven availability of online content [13, 14, 57, 96].”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [97]
The citation lists haven’t been fetched yet
Cited by 325
- Authors
- Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., & Jitsev, J.
- Venue
- Advances in Neural Information Processing Systems 35
- Published
- 2022
- Pages
- 25278-25294
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133250524
- Cited by
- 325
- References
- 0
- Citations by year
- 2026: 258 · 2025: 52 · 2024: 9 · 2023: 6
- FWCI
- 6.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- image-text models, image-text dataset, large-scale datasets, vision-language models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
