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..
Diffusion Models are Certifiably Robust Classifiers
Authors: Huanran Chen, Yinpeng Dong, Shitong Shao, Hao Zhongkai, Xiao Yang, Hang Su, Jun Zhu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the superior certified robustness of these Noised Diffusion Classifiers (NDCs). |
| Researcher Affiliation | Collaboration | Huanran Chen1,2, Yinpeng Dong1,2, Shitong Shao1, Zhongkai Hao1, Xiao Yang1, Hang Su1,3, Jun Zhu1,2 1Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua-Bosch Joint ML Center, THBI Lab BNRist Center, Tsinghua University, Beijing, 100084, China 2Real AI 3 Zhongguancun Laboratory, Beijing, China |
| Pseudocode | Yes | Algorithm 1 EPNDC |
| Open Source Code | Yes | Code is available at https://github.com/huanranchen/Noised Diffusion Classifiers. |
| Open Datasets | Yes | Following previous studies [2, 47, 52], we evaluate the certified robustness of our method on two standard datasets, CIFAR-10 [19] and Image Net [37], selecting a subset of 512 images from each. |
| Dataset Splits | No | The paper only mentions that "we trained a model" or refers to "standard settings" without elaboration - this applies here since they use off-the-shelf models, and thus do not specify the training splits for these models. They only specify the test sets for evaluation. |
| Hardware Specification | Yes | translating to about 3 * 10^6 seconds for certifying each image on a single 3090 GPU. |
| Software Dependencies | No | The paper only mentions software names without version numbers (e.g., "using Caffe", "the scikit-learn package") - This paper does not mention software dependencies with versions. |
| Experiment Setup | Yes | Experimental settings. Due to computational constraints, we employ a sample size of N = 10, 000 to estimate p A. ... To make a fair comparison with previous studies, we also select στ {0.25, 0.5, 1.0} for certification (thus τ is determined) and use EDM [16] as our diffusion models. |