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ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use adaptive momentum (ZO-AdaMM) instead of Mahalanobis-free ZO-SMD | ZO-SMD · ImageNet universal black-box attack, 100 images, T=40000 | attack success rate: 79 -> 84 | Helped |
| use ZO-AdaMM instead of ZO-SMD | ZO-SMD · ImageNet universal black-box attack, 100 images, T=40000 | final squared l2 distortion: 47.36 -> 38.40 | Helped |
| use ZO-AdaMM instead of ZO-NES (Euclidean projected sign-based) | ZO-NES · ImageNet universal black-box attack, 100 images, T=40000 | attack success rate: 74 -> 84 | Helped |
| use ZO-AdaMM instead of ZO-PSGD | ZO-PSGD · ImageNet universal black-box attack, 100 images, T=40000 | final squared l2 distortion: 49.92 -> 38.40 | Helped |
| use ZO-AdaMM instead of ZO-signSGD / ZO-SGD / ZO-SCD | unconstrained per-image attack · ImageNet per-image attack, 100 images | attack loss convergence and l2 distortion | Helped |
Closest papers by meaning
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- ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-Order Optimization (AAAI 2024 · similarity 0.87)
- DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training (ICLR 2024 · similarity 0.87)
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