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Methods › General › Semi-Supervised Learning Methods › Noisy Student

Noisy Student

38 papers tagged archive 2025-07-28

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

Noisy Student Training is a semi-supervised learning approach. It extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. It has three main steps:

  1. train a teacher model on labeled images
  2. use the teacher to generate pseudo labels on unlabeled images
  3. train a student model on the combination of labeled images and pseudo labeled images.

The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student.

Noisy Student Training seeks to improve on self-training and distillation in two ways. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Second, it adds noise to the student so the noised student is forced to learn harder from the pseudo labels. To noise the student, it uses input noise such as RandAugment data augmentation, and model noise such as dropout and stochastic depth during training.

Source: Self-training with Noisy Student improves ImageNet classificationSee Code · google-research/noisystudent

Papers archive 2025-07-28

30 shown of 38, 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 65 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
Speech Recognition10
speech-recognition10
Automatic Speech Recognition8
Automatic Speech Recognition (ASR)7
Image Classification6
image-classification6
Data Augmentation4
Pseudo Label4
Segmentation4
Knowledge Distillation3
Self-Supervised Learning3
Semantic Segmentation3
Classification2
Computed Tomography (CT)2
Domain Adaptation2
General Classification2
Language Modelling2
Object2
6D Pose Estimation1
6D Pose Estimation using RGB1

Usage over time archive 2025-07-28

Papers per year tagged with Noisy Student: 2019 to 2025, peak 10 10 0 2019: 1 paper 2019 2020: 8 papers 2020 2021: 10 papers 2021 2022: 8 papers 2022 2023: 0 papers 2023 2024: 8 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (38 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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