Methods › General › Adversarial Training › DropAttack
DropAttack
Introduced by Shiwen Ni et al. in DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DropAttack is an adversarial training method that adds intentionally worst-case adversarial perturbations to both the input and hidden layers in different dimensions and minimizes the adversarial risks generated by each layer.
Papers archive 2025-07-28
1 shown of 1, 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.
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DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks 29 Aug 2021 · 1 repository · arXiv:2108.12805
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Adversarial Attack | 1 |
| Adversarial Defense | 1 |
Usage over time archive 2025-07-28
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
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