Methods › General › Semi-Supervised Learning Methods › FixMatch

FixMatch

85 papers tagged archive 2025-07-28

Introduced by Kihyuk Sohn et al. in FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

FixMatch is an algorithm that first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image.

Description from: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Image credit: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

PaperSource

Papers archive 2025-07-28

30 shown of 85, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 85 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Pseudo Label20
Semi-Supervised Image Classification16
Image Classification8
Semantic Segmentation8
Contrastive Learning7
Representation Learning6
Segmentation6
image-classification6
Data Augmentation5
Semi-Supervised Semantic Segmentation5
Active Learning4
Classification4
Domain Adaptation4
Medical Image Segmentation4
Transfer Learning4
Domain Generalization3
Image Segmentation3
Semi-Supervised Domain Generalization3
Semi-supervised Medical Image Segmentation3
Unsupervised Domain Adaptation3

Usage over time archive 2025-07-28

Papers per year tagged with FixMatch: 2020 to 2025, peak 19 19 0 2020: 13 papers 2020 2021: 19 papers 2021 2022: 19 papers 2022 2023: 17 papers 2023 2024: 12 papers 2024 2025: 5 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (85 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Semi-Supervised Learning Methods

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