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 [1].
Toward Robust Spiking Neural Network Against Adversarial Perturbation
Authors: LING LIANG, Kaidi Xu, Xing Hu, Lei Deng, Yuan Xie
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed methods are evaluated on MNIST [18], FMNIST [32] and NMNIST [24] datasets.1 The experimental results show that we can achieve a maximum 37.7% attack error reduction with 3.7% original accuracy loss. |
| Researcher Affiliation | Collaboration | Ling Liang UC Santa Barbara EMAIL Kaidi Xu Drexel University EMAIL Xing Hu SKL of Processors Institute of Computing Technology, CAS EMAIL Lei Deng Tsinghua University EMAIL Yuan Xie Alibaba Group EMAIL |
| Pseudocode | Yes | Algorithm 1 S-IBP; Algorithm 2 S-CROWN |
| Open Source Code | Yes | Our proposed methods are evaluated on MNIST [18], FMNIST [32] and NMNIST [24] datasets1. 1https://github.com/liangling76/certify_snn |
| Open Datasets | Yes | Our proposed methods are evaluated on MNIST [18], FMNIST [32] and NMNIST [24] datasets1. 1https://github.com/liangling76/certify_snn |
| Dataset Splits | No | The paper mentions 'test data' but does not specify validation splits explicitly. 'In the original training, we adopt BPTT based training [31]. We train 80 epochs for each SNN model.' |
| Hardware Specification | Yes | The hardware we used is one Nvidia RTX3090 GPU and one AMD Ryzen CPU. |
| Software Dependencies | No | The paper states 'Our experiments are conducted by Pytorch 1.8.' This is one software component with a version, but it does not list multiple key components or a self-contained solver/package with specific version numbers. |
| Experiment Setup | Yes | In the original training, we adopt BPTT based training [31]. We train 80 epochs for each SNN model. The learning rate is set to 0.01 at the beginning, it decays to 0.001 at the 55th epoch. In robust training, we use the lower bound of S-CROWN as the loss function. During robust training, we set to 0 at the beginning. It will increase linearly to the ๏ฌnal during the ๏ฌrst 250 training epochs. In the last 50 training epochs, is unchanged. |