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..
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Authors: Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we demonstrate its effectiveness through extensive experimentation on Image Net and CIFAR-10. We verify the efficacy of our method through extensive experimentation on Image Net and CIFAR-10. We are able to convert pretrained Res Net-18/34/50 and Res Net-110, on CIFAR-10 and Image Net respectively, into certifiably robust models; our results are summarized in Tables 1 and 2 (details are in section 3). |
| Researcher Affiliation | Collaboration | Hadi Salman EMAIL Microsoft Research Mingjie Sun EMAIL CMU Greg Yang EMAIL Microsoft Research Ashish Kapoor EMAIL Microsoft Research J. Zico Kolter EMAIL CMU |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our code replicating all the experiments in the paper can be found at: https: //github.com/microsoft/denoised-smoothing1. |
| Open Datasets | Yes | extensive experimentation on Image Net and CIFAR-10. We are able to convert pretrained Res Net-18/34/50 and Res Net-110, on CIFAR-10 and Image Net respectively, into certifiably robust models |
| Dataset Splits | Yes | To assess the performance of our method on these APIs, we aggregate 100 random images from the Image Net validation set and certify their predictions across all four APIs. |
| Hardware Specification | No | No specific details about GPU or CPU models, memory, or cloud instance types used for experiments were provided in the main text. |
| Software Dependencies | No | The paper mentions software like 'Py Torch-pretrained Res Net' and denoisers like 'Dn CNN' and 'Mem Net', but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In the following experiments, we only report the results for σ = 0.25,8 and we report the best curves over the denoiser architectures mentioned above. For more details on the architectures of the classifiers/denoisers, training/certification hyperparameters, etc., we refer the reader to Appendix A. |