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Adversarial Model Perturbation

AMP

92 papers tagged archive 2025-07-28

Introduced by Yaowei Zheng et al. in Regularizing Neural Networks via Adversarial Model Perturbation

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

Based on the understanding that the flat local minima of the empirical risk cause the model to generalize better. Adversarial Model Perturbation (AMP) improves generalization via minimizing the AMP loss, which is obtained from the empirical risk by applying the worst norm-bounded perturbation on each point in the parameter space.

PaperSourceSee Code · hiyouga/AMP-Regularizer

Papers archive 2025-07-28

30 shown of 92, 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 72 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
Denoising7
compressed sensing6
regression6
Action Detection4
Activity Detection4
Bayesian Inference4
parameter estimation4
Benchmarking3
Graph Neural Network3
Prediction3
Protein Language Model3
Variational Inference3
model3
Clustering2
Decoder2
Distributed Computing2
Diversity2
Drug Discovery2
Quantization2
Uncertainty Quantification2

Usage over time archive 2025-07-28

Papers per year tagged with AMP: 2020 to 2025, peak 22 22 0 2020: 3 papers 2020 2021: 14 papers 2021 2022: 20 papers 2022 2023: 22 papers 2023 2024: 22 papers 2024 2025: 11 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (92 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

Optimization

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