Adversarial Training and Provable Robustness: A Tale of Two Objectives
Authors: Jiameng Fan, Wenchao Li7367-7376
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform both theoretical analysis on the convergence of the proposed technique and experimental comparison with state-of-the-arts. Results on MNIST and CIFAR-10 show that our method can consistently match or outperform prior approaches for provable l robustness. |
| Researcher Affiliation | Academia | Jiameng Fan , Wenchao Li Department of Electrical and Computer Engineering, Boston University, Boston {jmfan, wenchao}@bu.edu |
| Pseudocode | Yes | Algorithm 1 Weight Updates and Algorithm 2 Joint Training |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | Results on MNIST and CIFAR-10 show that our method can consistently match or outperform prior approaches for provable l robustness. |
| Dataset Splits | No | The paper mentions 'test dataset' and 'test examples' but does not provide specific training/validation/test dataset splits, percentages, or explicit sample counts for reproduction. |
| Hardware Specification | Yes | We perform all experiments on a desktop server using at most 4 Ge Force GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | Algorithm 2 Joint Training Input Warm-up epochs Tnat and Tadv, ϵtrain ramp-up epochs R, maximum FOSC value cmax... ct=clip(cmax (t R) cmax/T , 0, cmax)... κadv, κIBP, κreg=COMPUTE WEIGHTS(xadv, t, ct)... loss=κadv Ladv(θt)+κIBPLIBP(θt)+κreg LIBP(θt) 2 2... θt+1=θt ηtgfinal(θt) gfinal(θt): stochastic gradient |