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..
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
Authors: Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several datasets demonstrate the effectiveness of our approach for robustness certification against multiple kinds of semantic transformations and corruptions, which is not achievable by the alternative baselines. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science & Technology, Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University 3Real AI 5Tsinghua University-China Mobile Communications Group Co., Ltd. Joint Institute |
| Pseudocode | No | The paper describes algorithms conceptually and mathematically but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We use MNIST (Lecun et al., 1998), CIFAR-10, and CIFAR100 (Krizhevsky et al., 2009) datasets to verify our methods. |
| Dataset Splits | No | The paper mentions applying data augmentation but does not provide specific train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | Yes | The training process of classifiers and certification for semantic transformations are done on 2080Ti GPUs. |
| Software Dependencies | No | The paper mentions software components like U-Net, Batch Norm, Group Norm, and L1-loss but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | All models are trained by using an Adam optimizer (Kingma & Ba, 2015) with an initial learning rate of 0.001 that decays every 50 epochs until convergence. |