Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training
Authors: Jianhao Ding, Tong Bu, Zhaofei Yu, Tiejun Huang, Jian Liu
NeurIPS 2022 | Venue PDF | 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 EMAIL Tong Bu Institution for Artificial Intelligence School of Computer Science Peking University Beijing, China 100871 EMAIL Zhaofei Yu Institute for Artificial Intelligence School of Computer Science Peking University Beijing, China 100871 EMAIL Tiejun Huang School of Computer Science Peking University Beijing, China 100871 EMAIL Jian K. Liu School of Computing University of Leeds Leeds LS2 9JT EMAIL |
| 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. |