Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
Authors: Yujia Huang, Huan Zhang, Yuanyuan Shi, J. Zico Kolter, Anima Anandkumar
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we show that our method consistently outperforms state-of-the-art methods in both clean and certified accuracy on MNIST, CIFAR-10 and Tiny Image Net datasets with various network architectures. |
| Researcher Affiliation | Collaboration | Yujia Huang1 Huan Zhang2 Yuanyuan Shi3 J. Zico Kolter2,4 Anima Anandkumar1,5 1California Institute of Technology 2Carnegie Mellon University 3UC San Diego 4Bosch Center for AI 5NVIDIA |
| Pseudocode | Yes | Algorithm 1: Local Lipchitz based Certifiably Robust Training |
| Open Source Code | Yes | Our code is available at https://github. com/yjhuangcd/local-lipschitz. |
| Open Datasets | Yes | We train with our method to certify robustness within a 2 ball of radius 1.58 on MNIST [31] and 36/255 on CIFAR-10 [32] and Tiny-Imagenet 1 on various network architectures. 1https://tiny-imagenet.herokuapp.com |
| Dataset Splits | No | The paper states 'For more details and hyper-parameters in training, please refer to Appendix C.' However, it does not explicitly provide specific details on validation dataset splits, such as percentages, sample counts, or methodology for creating a validation set from the mentioned datasets in the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | For more details and hyper-parameters in training, please refer to Appendix C. |