The reviewed record of science sign in
Pith

arxiv: 2001.07685 · v2 · pith:6JPBCBLO · submitted 2020-01-21 · cs.LG · cs.CV· stat.ML

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Kihyuk Sohn , David Berthelot , Chun-Liang Li , Zizhao Zhang , Nicholas Carlini , Ekin D. Cubuk , Alex Kurakin , Han Zhang
show 1 more author
Colin Raffel
This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:6JPBCBLOrecord.jsonopen to challenge →

classification cs.LG cs.CVstat.ML
keywords fixmatchmodellearningperformancesemi-supervisedaccuracyconsistencyimage
0
0 comments X
read the original abstract

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling. Our algorithm, FixMatch, 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. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 -- just 4 labels per class. Since FixMatch bears many similarities to existing SSL methods that achieve worse performance, we carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch's success. We make our code available at https://github.com/google-research/fixmatch.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. VISTA: Variance-Gated Inter-Sequence Test-Time Adaptation for Multi-Sequence MRI Segmentation

    cs.CV 2026-05 conditional novelty 7.0

    VISTA is a source-free TTA framework for multi-sequence MRI segmentation that uses inter-sequence spectral/patch interventions and cross-view variance gating to handle modality-interaction shifts, reporting Dice gains...

  2. SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data

    cs.LG 2026-05 unverdicted novelty 7.0

    SeBA is a joint-embedding framework that separates tabular data into two complementary views and aligns one view's representations to the nearest-neighbor structure of the other, improving feature-label relationships ...

  3. A Simple Framework for Contrastive Learning of Visual Representations

    cs.LG 2020-02 accept novelty 7.0

    SimCLR learns visual representations by contrasting augmented views of the same image and reaches 76.5% ImageNet top-1 accuracy with a linear classifier, matching a supervised ResNet-50.

  4. Adapting Vision-Language Models from Iconic to Inclusive for Multi-Label Recognition Without Labels

    cs.CV 2026-06 unverdicted novelty 6.0

    An unsupervised adaptation framework for VLMs enables label-free multi-label image recognition by using multi-sampling to counter iconic bias and blend adaptation to match multi-label distributions, outperforming prio...

  5. VISTA: Variance-Gated Inter-Sequence Test-Time Adaptation for Multi-Sequence MRI Segmentation

    cs.CV 2026-05 unverdicted novelty 6.0

    VISTA is a test-time adaptation framework for multi-sequence MRI that uses inter-sequence intervention probes and cross-view disagreement variance to gate self-training, yielding Dice gains of +1.89% on low-field Afri...

  6. A Semi-Supervised Framework for Speech Confidence Detection using Whisper

    cs.SD 2026-05 unverdicted novelty 6.0

    A hybrid semi-supervised framework fusing Whisper embeddings with acoustic and prosodic features achieves 0.751 Macro-F1 for speaker confidence detection and outperforms baselines including WavLM, HuBERT, and Wav2Vec 2.0.

  7. Revisiting Feature Prediction for Learning Visual Representations from Video

    cs.CV 2024-02 conditional novelty 6.0

    V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.

  8. TopoPult-SSL: Gland-Mask-Free Cross-Device Meibomian Gland Segmentation via Self-Distilled Weak Clinical Priors

    cs.CV 2026-06 unverdicted novelty 5.0

    A two-stage framework adapts source models for cross-device meibomian gland segmentation using weak clinical priors and self-distillation, reaching Dice 0.716 on a 1000-to-100 image benchmark while enabling mask-free ...