Methods › Computer Vision › Generative Models › SAG

Self-Attention Guidance

SAG

22 papers tagged archive 2025-07-28

Introduced by Susung Hong et al. in Improving Sample Quality of Diffusion Models Using Self-Attention Guidance

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

The archive carries no description for this method.

PaperSource

Papers archive 2025-07-28

22 shown of 22, 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 25 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
Diversity2
Abstract generation1
Active Learning1
Anatomy1
Decision Making1
Denoising1
Diagnostic1
Evolutionary Algorithms1
Fault Detection1
Fault Diagnosis1
Federated Learning1
Image Generation1
Knowledge Distillation1
Multiple Instance Learning1
Node Classification1
Optical Flow Estimation1
Out-of-Distribution Generalization1
Outlier Detection1
Prediction1
Rhythm1

Usage over time archive 2025-07-28

Papers per year tagged with SAG: 2022 to 2025, peak 8 8 0 2022: 3 papers 2022 2023: 8 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 (22 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

Generative Models

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