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..
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100
Authors: Sahil Singla, Surbhi Singla, Soheil Feizi
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On CIFAR-10, we achieve significant improvements over prior works in provable robust accuracy (5.81%) with only a minor drop in standard accuracy ( 0.29%). Code for reproducing all experiments in the paper is available at https://github.com/singlasahil14/SOC. We perform experiments under the setting of provably robust image classification on CIFAR-10 and CIFAR-100 datasets... |
| Researcher Affiliation | Academia | Sahil Singla1, Surbhi Singla2, Soheil Feizi1 University of Maryland, College Park EMAIL1, surbhisingla1995@gmail.com2 |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks found. |
| Open Source Code | Yes | Code for reproducing all experiments in the paper is available at https://github.com/singlasahil14/SOC. |
| Open Datasets | Yes | We perform experiments under the setting of provably robust image classification on CIFAR-10 and CIFAR-100 datasets |
| Dataset Splits | Yes | Using 5000 held out samples from CIFAR-10, we tested 6 different values of γ shown in Table 3 and selected γ = 0.5 because it resulted in less than 0.5% decrease in standard accuracy while 4.96% increase in provably robust accuracy. |
| Hardware Specification | Yes | All experiments were performed using 1 NVIDIA GeForce RTX 2080 Ti GPU. |
| Software Dependencies | No | No specific software dependencies with version numbers are explicitly listed in the paper. |
| Experiment Setup | Yes | All networks were trained for 200 epochs with initial learning rate of 0.1, dropped by a factor of 0.1 after 100 and 150 epochs. For Certificate Regularization (or CR), we set the parameter γ = 0.5. |