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..
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness
Authors: Olukorede Fakorede, Modeste Atsague, Jin Tian
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct an extensive evaluation of the proposed method. To assess its versatility, we test it on various datasets, including CIFAR-10 (Krizhevsky et al., 2009), CIFAR-100 (Krizhevsky et al., 2009), SVHN (Netzer et al., 2011), and Tiny Image Net Deng et al. (2009). We apply simple data augmentations, such as 4-pixel padding with 32 32 random crop and random horizontal flip, to each of the datasets. Additionally, we employ Res Net-18 (He et al., 2016) and Wide Res Net-34-10 (He et al., 2016) as the backbone models. |
| Researcher Affiliation | Academia | Olukorede Fakorede EMAIL, EMAIL Department of Computer Science Iowa State University, Ames, Iowa, USA; Modeste Atsague EMAIL Department of Computer Science Iowa State University, Ames, Iowa,USA; Jin Tian EMAIL Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates |
| Pseudocode | Yes | Algorithm 1 AT-SDI Algorithm. Input: a neural network model with the parameters θ, step size κ, T PGD steps, a training dataset D of size n, |C| is the number of classes, and hyperparameter β. Output: a robust model with parameters θ; Algorithm 2 SDI-PGD Algorithm. Input: a neural network model with the parameters θ, step size κ, natural examples xi in a labelled dataset D of size n and |C| is the number of classes. Output: Adversarial examples x i |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing its code or a link to a code repository. |
| Open Datasets | Yes | To assess its versatility, we test it on various datasets, including CIFAR-10 (Krizhevsky et al., 2009), CIFAR-100 (Krizhevsky et al., 2009), SVHN (Netzer et al., 2011), and Tiny Image Net Deng et al. (2009). |
| Dataset Splits | No | We train the backbone networks using mini-batch gradient descent for 110 epochs, with a momentum of 0.9 and a batch size of 128. For training CIFAR-10, we used a weight decay of 5e-4, and for CIFAR-100, SVHN, and Tiny Image Net, we used a weight decay of 3.5e-3. The initial learning rate was set to 0.1 (0.01 for CIFAR-100, SVHN, and Tiny Image Net), and it was divided by 10 at the 75th epoch and then again at the 90th epoch. The hyperparameters are tuned using a validation set. |
| Hardware Specification | Yes | We conducted all experiments using a single core of an AMD EPYC 7513 processor, an Nvidia A100 SXM4 80 GB GPU, and 128 GB of RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We train the backbone networks using mini-batch gradient descent for 110 epochs, with a momentum of 0.9 and a batch size of 128. For training CIFAR-10, we used a weight decay of 5e-4, and for CIFAR-100, SVHN, and Tiny Image Net, we used a weight decay of 3.5e-3. The initial learning rate was set to 0.1 (0.01 for CIFAR-100, SVHN, and Tiny Image Net), and it was divided by 10 at the 75th epoch and then again at the 90th epoch. |