Review on self-supervised image recognition using deep neural networks
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Registry
Cites
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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?
Instagram is an example of weakly-supervised learning, where users' hash tags are used to label data.
“Instagram is an example of weakly-supervised learning, in which users' hash tags are used to label data [2].”
From Self-supervised learning: A succinct review (2023) · cites this paper as [2]
SSL requires labels but does not require human labeling, which distinguishes it from unsupervised learning.
“SSL differs from unsupervised learning in that it requires labels but does not require human labeling [2].”
From Self-supervised learning: A succinct review (2023) · cites this paper as [2]
The citation lists haven’t been fetched yet
References 106 · Cited by 179
- Authors
- Ohri, K., & Kumar, M.
- Affiliations
- National Institute of Technology Patna (IN)
- Corresponding author
- Ohri, Kriti
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 224
- Pages
- 107090
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W3157752331
- Cited by
- 179
- References
- 106
- Citations by year
- 2026: 13 · 2025: 32 · 2024: 44 · 2023: 52 · 2022: 30 · 2021: 8
- FWCI
- 11.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- self-supervised learning, image recognition, visual feature learning, pretext tasks, contrastive learning, clustering, image classification, object detection, image segmentation, unlabeled data, annotation efficiency
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.
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