mlsanityData to 13 Oct 2026

Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks

arXiv 2210.09919 ↗First posted 18 Oct 2022cs.CV cs.LGcited by 4

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Abstract (arXiv)

We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks combining pseudo-labeling and consistency regularization via strong data augmentation. We enable the application of FixMatch in semi-supervised learning problems beyond image classification by adding a matching operation on the pseudo-labels. This allows us to still use the full strength of data augmentation pipelines, including geometric transformations. We evaluate it on semi-supervised semantic segmentation on Cityscapes and Pascal VOC with different percentages of labeled data and ablate design choices and hyper-parameters. Dense FixMatch significantly improves results compared to supervised learning using only labeled data, approaching its performance with 1/4 of the labeled samples.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 34 of its 43 references that are in the map. First posted on arXiv 18 Oct 2022.

Builds on

34 of 43 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Multi-Task Label Discovery via Hierarchical Task Tokens for Partially Annotated Dense Predictionscited by 4
  2. 2025Knowledge Consultation for Semi-Supervised Semantic Segmentationcited by 1
  3. 2025AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learningcited by 1
  4. 2026Multi-task learning on partially labeled datasets via invariant/equivariant semi-supervised learningcited by 0