Who Cited It

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

2020 · arXiv (Cornell University) · 2,271 citations · 2 from inside this corpus

Kihyuk Sohn, David Berthelot low, Chunliang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, А.В. Куракин low, Han Zhang, Colin Raffel

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence (2020)FixMatch: Simplifying Semi-Su…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…Dropout: a simple way to prevent neural networks from overfitting (2014)Dropout: a simple way to prev…mixup: Beyond Empirical Risk Minimization (2017)mixup: Beyond Empirical Risk …Semi-Supervised Learning (2006)Semi-Supervised LearningSemi-Supervised Learning Literature Survey (2005)Semi-Supervised Learning Lite…Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019)Exploring the Limits of Trans…SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition (2019)SpecAugment: A Simple Data Au…On the importance of initialization and momentum in deep learning (2013)On the importance of initiali…Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised … (2018)Virtual Adversarial Training:…Introduction to Semi-Supervised Learning (2009)Introduction to Semi-Supervis…Unsupervised Data Augmentation for Consistency Training (2019)Unsupervised Data Augmentatio…Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training (2018)Unsupervised Domain Adaptatio…A Simple Framework for Contrastive Learning of Visual Representations (2020)A Simple Framework for Contra…Bootstrap your own latent: A new approach to self-supervised Learning (2020)Bootstrap your own latent: A …
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What this paper cites, inside the corpus

PaperYearCited
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
Dropout: a simple way to prevent neural networks from overfitting201434,236
mixup: Beyond Empirical Risk Minimization20174,806
Semi-Supervised Learning20064,334
Semi-Supervised Learning Literature Survey20053,879
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer20193,698
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition20193,619
On the importance of initialization and momentum in deep learning20133,523
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised …20182,873
Introduction to Semi-Supervised Learning20091,812
Unsupervised Data Augmentation for Consistency Training20191,624
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training20181,499

What cites it, inside the corpus

PaperYearCited
A Simple Framework for Contrastive Learning of Visual Representations20207,341
Bootstrap your own latent: A new approach to self-supervised Learning20203,447

Links

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Topics

Domain Adaptation and Few-Shot LearningComputer Science
Multimodal Machine Learning ApplicationsComputer Science
Advanced Neural Network ApplicationsComputer Science

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partial

One field of this record is missing or disagrees with another. What is shown below is what the source publishes.

  • supports9 author record(s) attached.
  • supports60 reference(s) recorded.
  • weakensThe DOI names 2001 but the record dates this to 2,020. One of the two is about a different paper.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:50+00:00.

sha256 86a6d6715ff540a0…