Methods › Computer Vision › Image Data Augmentation › Random Scaling

Random Scaling

13 papers tagged archive 2025-07-28

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

Random Scaling is a type of image data augmentation in which we randomly change the scale of the image within a specified range.

The Albumentations library has generalization of the RandomScaling called Affine

Affine transform allows randomly scale as RandomScaling, but you may also randomly rotate, translate, and shear.

Papers archive 2025-07-28

13 shown of 13, 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 27 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
Semantic Segmentation5
Segmentation4
Image Segmentation3
Instance Segmentation2
Object2
Object Detection2
Panoptic Segmentation2
Thermal Image Segmentation2
object-detection2
2D Semantic Segmentation1
Crowd Counting1
Data Augmentation1
Dichotomous Image Segmentation1
Edge-computing1
Federated Learning1
GPU1
Keypoint Estimation1
Knowledge Distillation1
Model Compression1
OpenAI Gym1

Usage over time archive 2025-07-28

Papers per year tagged with Random Scaling: 2016 to 2025, peak 3 3 0 2016: 1 paper 2016 2017: 1 paper 2017 2018: 1 paper 2018 2019: 3 papers 2019 2020: 1 paper 2020 2021: 2 papers 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 2 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (13 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

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections