Methods › Computer Vision › Image Data Augmentation › PAA

Patch AutoAugment

PAA

10 papers tagged archive 2025-07-28

Introduced by Shiqi Lin et al. in Local Patch AutoAugment with Multi-Agent Collaboration

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

Patch AutoAugment is a patch-level automatic data augmentation algorithm that automatically searches for the optimal augmentation policies for the patches of an image. Specifically, PAA allows each patch DA operation to be controlled by an agent and models it as a Multi-Agent Reinforcement Learning (MARL) problem. At each step, PAA samples the most effective operation for each patch based on its content and the semantics of the whole image. The agents cooperate as a team and share a unified team reward for achieving the joint optimal DA policy of the whole image. PAA is co-trained with a target network through adversarial training. At each step, the policy network samples the most effective operation for each patch based on its content and the semantics of the image.

PaperSource

Papers archive 2025-07-28

10 shown of 10, 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 21 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
Object Detection3
object-detection3
2D Object Detection1
Continual Learning1
Data Augmentation1
Deep Learning1
Dialogue Generation1
Diversity1
Ensemble Learning1
Fine-Grained Image Recognition1
Fracture detection1
Image Classification1
Image Generation1
Medical Object Detection1
Multi-agent Reinforcement Learning1
Object1
Super-Resolution1
TAG1
Transfer Learning1
Zero-Shot Learning1

Usage over time archive 2025-07-28

Papers per year tagged with PAA: 2021 to 2025, peak 4 4 0 2021: 3 papers 2021 2022: 1 paper 2022 2023: 4 papers 2023 2024: 1 paper 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (10 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