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A Survey on Semi-, Self- and Unsupervised Learning for Image Classification
Open accessAbstract
While deep learning strategies achieve outstanding results in computer vision tasks, one issue remains: The current strategies rely heavily on a huge amount of labeled data. In many real-world problems, it is not feasible to create such an amount of labeled training data. Therefore, it is common to incorporate unlabeled data into the training process to reach equal results with fewer labels. Due to a lot of concurrent research, it is difficult to keep track of recent developments. In this survey, we provide an overview of often used ideas and methods in image classification with fewer labels. We compare 34 methods in detail based on their performance and their commonly used ideas rather than a fine-grained taxonomy. In our analysis, we identify three major trends that lead to future research opportunities. 1. State-of-the-art methods are scalable to real-world applications in theory but issues like class imbalance, robustness, or fuzzy labels are not considered. 2. The degree of supervision which is needed to achieve comparable results to the usage of all labels is decreasing and therefore methods need to be extended to settings with a variable number of classes. 3. All methods share some common ideas but we identify clusters of methods that do not share many ideas. We show that combining ideas from different clusters can lead to better performance.
Overview
CoChat Summary
TL;DR
This survey reviews semi-, self-, and unsupervised learning for image classification, comparing 34 methods by their shared ideas and reported performance rather than a fine-grained taxonomy. It identifies three trends: limited real-world scalability, decreasing supervision requirements, and benefits from combining ideas across method clusters.
Key findings
- Across four datasets, reduced supervised methods approach supervised performance: on CIFAR-10, MixMatch and FixMatch exceed 95% accuracy and are roughly 3% below the fully supervised baseline; on CIFAR-100, FixMatch reaches about 77% versus a supervised baseline of about 80%.
- On STL-10, EnAET achieves more than 95%, and FixMatch reports nearly 95% with only 1,000 labels; reduced supervised methods can surpass the supervised baseline by about 20% due to the additional unlabeled data.
- On ILSVRC-2012, SimCLRv2 achieves the best reported Top-1 accuracy of 80.9% and Top-5 accuracy around 96%; only SimCLR, BYOL, and SimCLRv2 are less than 10% worse than the supervised baseline of around 86%.
- The survey concludes that reduced supervised learning reaches comparable results using roughly 10% of labels, and that combining ideas from different clusters can improve performance.
Methods
- Survey design: compares 34 methods for image classification, grouping them by training strategy—one-stage-semi-supervised, one-stage-unsupervised, and multi-stage-semi-supervised—and by common ideas such as cross-entropy, pseudo-labels, pretext tasks, contrastive loss, mutual information, overclustering, and mixup.
- Data and evaluation: analyzes reported results on CIFAR-10, CIFAR-100, STL-10, and ILSVRC-2012, using classification accuracy or cluster accuracy for unsupervised methods.
- Analysis: relies on results reported in the original papers or cross-references rather than recreating every method in a unified setup, and focuses on general trends and specialties instead of small performance differences.
Limitations
- Stated: the comparison uses reported values rather than a unified reimplementation, and differences in architectures, implementations, and adherence to semi-supervised evaluation guidelines may affect comparability.
- Stated: the common-idea framework is fuzzy and incomplete by definition, and the survey excludes related topics such as metric learning, meta learning, GANs, graph networks, pose estimation, segmentation, videos, sketches, and few-/zero-shot learning.
- Evident: the survey does not claim a complete method list and does not present new experiments; its conclusions depend on the availability and consistency of results reported by the reviewed papers.
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A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For CIFAR-10, CIFAR-100 and ILSVRC-2012 we have a gap of less than 5% left between total supervised and reduced supervised learning.”
§ A Survey on Semi-, Self-and Unsupervised Learning for Image Classification > 2) TREND: HOW MUCH SUPERVISION IS NEEDED?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The unsupervised methods are separated from the supervised baseline by a clear margin of up to 10%.”
§ A Survey on Semi-, Self-and Unsupervised Learning for Image Classification > TABLE 2.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the comparison, we identified that few common ideas are shared by one-stage-semi-supervised and multi-stagesemi-supervised methods.”
§ A Survey on Semi-, Self-and Unsupervised Learning for Image Classification > 3) TREND: COMBINATION OF COMMON IDEAS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For the STL-10 dataset, most methods report a better result than the supervised baseline.”
§ A Survey on Semi-, Self-and Unsupervised Learning for Image Classification > COMPARISON CONCERNING PERFORMANCE
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on 2 assumptions not shown here.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The performance gap between supervised and semi-or self-supervised methods is closing and the number of labels to get comparable results to fully supervised learning is decreasing.”
§ A Survey on Semi-, Self-and Unsupervised Learning for Image Classification > V. CONCLUSION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
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Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A Survey on Semi-, Self-and Unsupervised Learning for Image Classification > COMPARISON CONCERNING USED COMMON IDEAS
“All one-stage-semi-supervised methods use a cross-entropy loss during training”
§ A Survey on Semi-, Self-and Unsupervised Learning for Image Classification > COMPARISON CONCERNING USED COMMON IDEAS
“All one-stage-semi-supervised methods use no CE and often use a pretext task.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A Survey on Semi-, Self-and Unsupervised Learning for Image Classification > MEAN SQUARED ERROR (MSE)
“= H (P(•|f (x)) + H (P(•|f (x)) | P(•|f (y))) (5”
§ A Survey on Semi-, Self-and Unsupervised Learning for Image Classification > V. CONCLUSION
“MI balances the effects of maximizing the entropy with a uniform distribution for P(•|f (x)) and minimizing the conditional entropy by equalizing P(•|f (x)) and P(•|f (y)).”
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Metadata
- Authors
- Schmarje, L., Santarossa, M., Schroder, S.-M., & Koch, R.
- Affiliations
- Christian-Albrechts-Universität zu Kiel (DE)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 82146-82168
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3171229070
- Cited by
- 246
- References
- 120
- Citations by year
- 2026: 24 · 2025: 32 · 2024: 54 · 2023: 51 · 2022: 47 · 2021: 31
- FWCI
- 13.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- MACAU: Open Access Repository of Kiel University (University Library Kiel) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Land Schleswig-Holstein through the Open Access Publikationsfonds Funding Program
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- image classification, semi-supervised learning, self-supervised learning, unsupervised learning, limited labeled data, unlabeled data, class imbalance, fuzzy labels, method comparison
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.
A language model drew the claims from the paper on 2026-11-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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 ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
