Diffusion Models are Certifiably Robust Classifiers
Authors: Huanran Chen, Yinpeng Dong, Shitong Shao, Hao Zhongkai, Xiao Yang, Hang Su, Jun Zhu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |