Accelerating Certified Robustness Training via Knowledge Transfer
Authors: Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on CIFAR-10 show that CRT speeds up certified robustness training by 8 on average across three different architecture generations while achieving comparable robustness to state-of-the-art methods. We also show that CRT can scale to large-scale datasets like Image Net. |
| Researcher Affiliation | Collaboration | Pratik Vaishnavi Stony Brook University pvaishnavi@cs.stonybrook.edu Kevin Eykholt IBM Research kheykholt@ibm.com Amir Rahmati Stony Brook University amir@cs.stonybrook.edu |
| Pseudocode | Yes | Algorithm 1 Certified Robustness Transfer (CRT) |
| Open Source Code | Yes | 3. (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Appendix ??. 4. (c) Did you include any new assets either in the supplemental material or as a URL? [Yes] See Appendix ??. |
| Open Datasets | Yes | Our main results are generated using the CIFAR-10 dataset [18], but we also demonstrate the effectiveness of CRT on Image Net [5] (Section 5.3). Both these datasets are open-source and free for non-commercial use. |
| Dataset Splits | No | On CIFAR-10, we compute these metrics using the entire test set. The paper does not specify how the training and validation splits were created or their sizes. |
| Hardware Specification | Yes | All classifiers were trained on the same machine with a single Nvidia Titan V GPU. |
| Software Dependencies | No | The paper mentions training with 'Stochastic Gradient Descent' and using 'pytorch-cifar' (implying PyTorch), but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | All CRT classifiers were trained using Stochastic Gradient Descent till convergence (200 epochs), with a batch size of 128. Further hyperparameter details are available in Appendix ??. |