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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Enhancing the Robustness of Spiking Neural Networks with Stochastic Gating Mechanisms
Authors: Jianhao Ding, Zhaofei Yu, Tiejun Huang, Jian K. Liu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results prove that our method can be used alone or with existing robust enhancement algorithms to improve SNN robustness and reduce SNN energy consumption. |
| Researcher Affiliation | Academia | 1School of Computer Science, Peking University 2Institution for Artificial Intelligence, Peking University 3School of Computer Science, University of Birmingham |
| Pseudocode | No | The paper describes its methods in prose and uses mathematical equations, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Ding Jianhao/Sto G-meets-SNN/. |
| Open Datasets | Yes | To verify the effectiveness of our method, we conduct experiments on the CIFAR-10 and CIFAR100 datasets (Krizhevsky, Hinton et al. 2009). |
| Dataset Splits | No | The paper mentions using CIFAR-10 and CIFAR-100 datasets but does not explicitly state the training, validation, or test split percentages or sample counts within the main text. |
| Hardware Specification | Yes | The experiments are conducted on GPU devices of the NVIDIA RTX 3090 with PyTorch (v1.12.1). |
| Software Dependencies | Yes | The experiments are conducted on GPU devices of the NVIDIA RTX 3090 with PyTorch (v1.12.1). |
| Experiment Setup | Yes | To punish Po, we set γ = 5 10 6 by default. We train our model with white-box FGSM adversarial examples on each mini-batch of images. The perturbation boundary is 2/255 (Kundu, Pedram, and Beerel 2021). The EOT step is set to 10 by default... The intensity of the FGSM attack is 8/255. For the PGD-l attack, the overall intensity, step number, and step size are fixed to 8/255, 7, and 0.01, respectively. |