Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
Authors: Anh Tuan Bui, Trung Le, He Zhao, Paul Montague, Olivier deVel, Tamas Abraham, Dinh Phung6831-6839
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive and comprehensive experiments to demonstrate that our proposed method outperforms the state-of-the-art ensemble baselines, at the same time can detect a wide range of adversarial examples with a nearly perfect accuracy. Our code is available at: https://github.com/tuananhbui89/Crossing-Collaborative Ensemble. |
| Researcher Affiliation | Collaboration | Anh Tuan Bui1, Trung Le1, He Zhao1 Paul Montague2, Olivier de Vel2, Tamas Abraham2, Dinh Phung1 1Monash University, Australia 2Defence Science and Technology Group, Australia |
| Pseudocode | No | The paper describes the proposed method using prose and mathematical equations but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | Yes | Our code is available at: https://github.com/tuananhbui89/Crossing-Collaborative Ensemble. |
| Open Datasets | Yes | We use CIFAR10 and CIFAR100 as the benchmark datasets in our experiment. |
| Dataset Splits | No | The paper states 'Both datasets have 50,000 training images and 10,000 test images,' which defines the training and test sets, but does not explicitly provide details for a separate validation split or how it was derived. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory specifications, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'cleverhans', 'foolbox', and 'tensorflow' but does not provide specific version numbers for these or any other key software components used in the experiments. |
| Experiment Setup | Yes | Specifically, the configuration for the CIFAR10 dataset is k = 10, ϵ = 8/255, η = 2/255 and that for the CIFAR100 dataset is k = 10, ϵ = 0.01, η = 0.001. For the CIFAR10 dataset with Res Net architecture, we use the same setting in (Pang et al. 2019) which is k = 10, ϵ U(0.01, 0.05), η = ϵ/10. |