Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
Authors: Mahyar Fazlyab, Taha Entesari, Aniket Roy, Rama Chellappa
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the MNIST, CIFAR-10, and Tiny-Image Net data sets verify that our proposed algorithm obtains competitively improved results compared to the state-of-the-art. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering Johns Hopkins University {mahyarfazlyab, tentesa1, aroy28, rchella4}@jhu.edu |
| Pseudocode | No | No structured pseudocode or algorithm blocks are explicitly presented in the paper. |
| Open Source Code | Yes | Code available on https://github.com/o4lc/CRM-Lip LT. |
| Open Datasets | Yes | Experiments on the MNIST [46], CIFAR-10 [47] and Tiny-Imagement [48] data sets verify that our proposed algorithm obtains competitively improved results compared to the state-of-the-art. |
| Dataset Splits | No | The paper mentions training on MNIST, CIFAR-10, and Tiny-ImageNet, and evaluates on a 'test dataset', but it does not explicitly provide specific training/test/validation dataset split percentages, counts, or a clear methodology for splitting beyond general references to test data. |
| Hardware Specification | No | The paper mentions parallelized implementation on GPUs, but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The details of the architectures, training process, and most hyperparameters are deferred to the supplementary materials, meaning specific experimental setup details are not present in the main text. |