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..
Consistency Regularization for Certified Robustness of Smoothed Classifiers
Authors: Jongheon Jeong, Jinwoo Shin
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments under various deep neural network architectures and datasets show that the certified ℓ2-robustness can be dramatically improved with the proposed regularization, even achieving better or comparable results to the state-of-the-art approaches with significantly less training costs and hyperparameters. |
| Researcher Affiliation | Academia | School of Electrical Engineering Graduate School of AI Korea Advanced Institute of Science and Technology (KAIST) Daejeon, South Korea |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Code is available at https://github.com/jh-jeong/smoothing-consistency. |
| Open Datasets | Yes | We verify the effectiveness of our proposed regularization based on extensive evaluation covering MNIST [21], CIFAR-10 [20], and Image Net [30] classification datasets. |
| Dataset Splits | No | The paper mentions training and testing on datasets like CIFAR-10 and ImageNet, and refers to following training details from prior works [10, 32], but does not explicitly provide specific train/validation/test dataset splits within its own text. |
| Hardware Specification | Yes | In this experiment, every model is trained on CIFAR-10 using one GPU of NVIDIA TITAN X (Pascal). |
| Software Dependencies | No | The paper mentions using well-known models like ResNet but does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For a fair comparison, we follow the same training details used in Cohen et al. [10] and Salman et al. [32]. For each model configuration, we consider three different models as varying the noise level σ {0.25, 0.5, 1.0}. During inference, we apply randomized smoothing with the same σ used in the training. When our regularization is used, we use m = 2 and η = 0.5 unless otherwise specified. |