Methods › Computer Vision › Image Data Augmentation › Fast AutoAugment

Fast AutoAugment

7 papers tagged archive 2025-07-28

Introduced by Sungbin Lim et al. in Fast AutoAugment

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

Fast AutoAugment is an image data augmentation algorithm that finds effective augmentation policies via a search strategy based on density matching, motivated by Bayesian DA. The strategy is to improve the generalization performance of a given network by learning the augmentation policies which treat augmented data as missing data points of training data. However, different from Bayesian DA, the proposed method recovers those missing data points by the exploitation-and-exploration of a family of inference-time augmentations via Bayesian optimization in the policy search phase. This is realized by using an efficient density matching algorithm that does not require any back-propagation for network training for each policy evaluation.

PaperSourceSee Code · kakaobrain/fast-autoaugment

Papers archive 2025-07-28

7 shown of 7, 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

14 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
Data Augmentation7
Image Classification4
GPU2
image-classification2
AutoML1
BIG-bench Machine Learning1
Classification1
General Classification1
Image Augmentation1
Object1
Object Detection1
Reinforcement Learning1
model1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with Fast AutoAugment: 2019 to 2025, peak 4 4 0 2019: 1 paper 2019 2020: 4 papers 2020 2021: 0 papers 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 0 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (7 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