Methods › General › Regularization › Off-Diagonal Orthogonal Regularization

Off-Diagonal Orthogonal Regularization

132 papers tagged archive 2025-07-28

Introduced by Andrew Brock et al. in Large Scale GAN Training for High Fidelity Natural Image Synthesis

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

Off-Diagonal Orthogonal Regularization is a modified form of orthogonal regularization originally used in BigGAN. The original orthogonal regularization is known to be limiting so the authors explore several variants designed to relax the constraint while still imparting the desired smoothness to the models. They opt for a modification where they remove diagonal terms from the regularization, and aim to minimize the pairwise cosine similarity between filters but does not constrain their norm:

Rᵦ(W) = β|| WᵀW ⊙(1-I) ||²_F

where 1 denotes a matrix with all elements set to 1. The authors sweep β values and select 10⁻⁴.

PaperSourceSee Code · ajbrock/BigGAN-PyTorch

Papers archive 2025-07-28

30 shown of 132, 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 141 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
Image Generation37
Conditional Image Generation15
Generative Adversarial Network11
reinforcement-learning9
Multi-agent Reinforcement Learning7
Reinforcement Learning7
Data Augmentation6
Reinforcement Learning (RL)6
Super-Resolution6
Vocal Bursts Intensity Prediction6
Decision Making5
Unconditional Image Generation5
Attribute4
Clustering4
Denoising4
Diversity4
Object4
Transfer Learning4
Benchmarking3
Decoder3

Usage over time archive 2025-07-28

Papers per year tagged with Off-Diagonal Orthogonal Regularization: 2018 to 2024, peak 37 37 0 2018: 2 papers 2018 2019: 10 papers 2019 2020: 28 papers 2020 2021: 20 papers 2021 2022: 33 papers 2022 2023: 37 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (132 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

Regularization

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