Papers › Regularizing Neural Networks via Adversarial Model Perturbation
Regularizing Neural Networks via Adversarial Model Perturbation
Yaowei Zheng, Richong Zhang, Yongyi Mao
Effective regularization techniques are highly desired in deep learning for alleviating overfitting and improving generalization. This work proposes a new regularization scheme, based on the understanding that the flat local minima of the empirical risk cause the model to generalize better. This scheme is referred to as adversarial model perturbation (AMP), where instead of directly minimizing the empirical risk, an alternative "AMP loss" is minimized via SGD. Specifically, the AMP loss is obtained from the empirical risk by applying the "worst" norm-bounded perturbation on each point in the parameter space. Comparing with most existing regularization schemes, AMP has strong theoretical justifications, in that minimizing the AMP loss can be shown theoretically to favour flat local minima of the empirical risk. Extensive experiments on various modern deep architectures establish AMP as a new state of the art among regularization schemes. Our code is available at https://github.com/hiyouga/AMP-Regularizer.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | PyramidNet + AA (AMP) | Percentage correct | 98.02 | #56 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | PreActResNet18 (AMP) | Percentage correct | 96.03 | #122 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | PyramidNet + AA (AMP) | Percentage correct | 86.64 | #51 of 211 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | PreActResNet18 (AMP) | Percentage correct | 78.49 | #137 of 211 | Archive leaderboard | report |
| Image Classification | SVHN | PyramidNet + AA (AMP) | Percentage error | 1.35 | #7 of 62 | Archive leaderboard | report |
| Image Classification | SVHN | PreActResNet18 (AMP) | Percentage error | 2.30 | #33 of 62 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: AMP
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