Methods › Computer Vision › Image Data Augmentation › Random Gaussian Blur

Random Gaussian Blur

260 papers tagged archive 2025-07-28

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

Random Gaussian Blur is an image data augmentation technique where we randomly blur the image using a Gaussian distribution.

Image Source: Wikipedia

Papers archive 2025-07-28

30 shown of 260, 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 204 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
Self-Supervised Learning129
Contrastive Learning124
Representation Learning83
Data Augmentation31
Image Classification27
image-classification20
Semantic Segmentation16
Transfer Learning16
Linear evaluation14
Object Detection13
object-detection13
Classification9
Clustering8
Retrieval8
Segmentation8
Activity Recognition7
General Classification7
Human Activity Recognition7
Self-Supervised Image Classification6
Benchmarking5

Usage over time archive 2025-07-28

Papers per year tagged with Random Gaussian Blur: 2016 to 2025, peak 59 59 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 1 paper 2018 2019: 0 papers 2019 2020: 29 papers 2020 2021: 59 papers 2021 2022: 58 papers 2022 2023: 53 papers 2023 2024: 48 papers 2024 2025: 11 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (260 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

Image Data Augmentation

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