SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training
Authors: Jianhao Ding, Tong Bu, Zhaofei Yu, Tiejun Huang, Jian Liu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on the image recognition benchmarks have proven that our training scheme can defend against powerful adversarial attacks crafted from strong differentiable approximations. |
| Researcher Affiliation | Academia | Jianhao Ding School of Computer Science Peking University Beijing, China 100871 djh01998@stu.pku.edu.cn Tong Bu Institution for Artificial Intelligence School of Computer Science Peking University Beijing, China 100871 putong30@pku.edu.cn Zhaofei Yu Institute for Artificial Intelligence School of Computer Science Peking University Beijing, China 100871 yuzf12@pku.edu.cn Tiejun Huang School of Computer Science Peking University Beijing, China 100871 tjhuang@pku.edu.cn Jian K. Liu School of Computing University of Leeds Leeds LS2 9JT j.liu9@leeds.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/putshua/SNN-RAT. |
| Open Datasets | Yes | We validate our proposed robust SNN training scheme on the image classification tasks, where the CIFAR-10 and CIFAR-100 datasets are used. ... Public datasets. |
| Dataset Splits | No | The paper mentions using CIFAR-10 and CIFAR-100 datasets but does not explicitly provide the specific training/validation/test split percentages or sample counts in the provided text. |
| Hardware Specification | No | The paper states that compute resources were included in the overall submission, but the provided text does not contain specific hardware details such as GPU/CPU models or memory specifications used for running experiments. |
| Software Dependencies | No | The paper mentions training methods and algorithms but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We set β = 0.001 and 0.004 for VGG-11 and Wide Res Net-16, respectively. The perturbation boundary ϵ is set to 2/255 when training models. ... Without specific instructions, we set ϵ to 8/255 for all methods for the purpose of testing. For iterative methods like PGD and BIM, the attack step α = 0.01, and the step number is 7. |