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Fast Bi-level Adversarial Training

Fast_BAT

1 paper tagged archive 2025-07-28

Introduced by Yihua Zhang et al. in Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization

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

Fast-BAT is a new method for accelerated adversarial training.

PaperSource

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.

Tasks archive 2025-07-28

1 task the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Adversarial Defense1

Usage over time archive 2025-07-28

Papers per year tagged with Fast_BAT: 2021 to 2021, peak 1 1 0 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (1 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

Adversarial Training

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