Confidence-Aware Training of Smoothed Classifiers for Certified Robustness
Authors: Jongheon Jeong, Seojin Kim, Jinwoo Shin
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the proposed method, despite its simplicity, consistently exhibits improved certified robustness upon state-of-the-art training methods. We evaluate the effectiveness of our proposed training scheme based on various well-established image classification benchmarks to measure robustness, including MNIST (Le Cun et al. 1998), Fashion-MNIST (Xiao, Rasul, and Vollgraf 2017), CIFAR-10/100 (Krizhevsky 2009), and Image Net (Russakovsky et al. 2015) (for certified robustness)5, as well as MNIST-C (Mu and Gilmer 2019)6 and CIFAR-10C (Hendrycks and Dietterich 2019) (for corruption robustness). |
| Researcher Affiliation | Academia | Jongheon Jeong*, Seojin Kim*, Jinwoo Shin Korea Advanced Institute of Science and Technology (KAIST) Daejeon, 34141 South Korea {jongheonj, osikjs, jinwoos}@kaist.ac.kr |
| Pseudocode | Yes | The complete procedure of computing our proposed CAT-RS loss can be found in Algorithm 1 of Appendix A. |
| Open Source Code | Yes | Code is available at https://github.com/alinlab/smoothing-catrs. |
| Open Datasets | Yes | We evaluate the effectiveness of our proposed training scheme based on various well-established image classification benchmarks to measure robustness, including MNIST (Le Cun et al. 1998), Fashion-MNIST (Xiao, Rasul, and Vollgraf 2017), CIFAR-10/100 (Krizhevsky 2009), and Image Net (Russakovsky et al. 2015) (for certified robustness)5... |
| Dataset Splits | Yes | We evaluate the performance on the uniformly-subsampled 500 samples in the Image Net validation dataset following (Cohen, Rosenfeld, and Kolter 2019; Jeong and Shin 2020; Salman et al. 2019; Jeong et al. 2021). |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For a fair comparison, we follow the standard protocol and training setup of the previous works (Cohen, Rosenfeld, and Kolter 2019; Zhai et al. 2020; Jeong and Shin 2020).7 More details, e.g., training setups, datasets, and hyperparameters, can be found in Appendix B. We use n = 100, 000, n0 = 100, and α = 0.001 for CERTIFY, following previous works. |