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..
DAFA: Distance-Aware Fair Adversarial Training
Authors: Hyungyu Lee, Saehyung Lee, Hyemi Jang, Junsung Park, Ho Bae, Sungroh Yoon
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results across various datasets demonstrate that our method not only maintains average robust accuracy but also significantly improves the worst robust accuracy, indicating a marked improvement in robust fairness compared to existing methods. We conduct both theoretical and empirical analyses of robust fairness, taking into account inter-class similarity. Experiments across multiple datasets validate that our approach effectively boosts the worst robust accuracy. |
| Researcher Affiliation | Academia | Hyungyu Lee1, Saehyung Lee1, Hyemi Jang1, Junsung Park1, Ho Bae2,*, and Sungroh Yoon1,3,* 1Electrical and Computer Engineering, Seoul National University 2Department of Cyber Security, Ewha Womans University 3Interdisciplinary Program in Artificial Intelligence, Seoul National University |
| Pseudocode | Yes | A THE ALGORITHMS OF DAFA Algorithm 1 Training procedure of DAFA ... Algorithm 2 DAFAcomp |
| Open Source Code | Yes | Our code is available at https://github.com/rucy74/DAFA. |
| Open Datasets | Yes | We conducted experiments on CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009), and STL-10 (Coates et al., 2011) |
| Dataset Splits | No | The paper mentions 50,000 training images and 10,000 test images for CIFAR-10/100, and 5,000 training images and 8,000 test images for STL-10. It does not explicitly define a separate validation set split or describe a validation procedure for its own experiments, apart from mentioning prior work that uses training and validation performances. |
| Hardware Specification | Yes | Our experiments were carried out on a single RTX 8000 GPU equipped with CUDA11.6 and Cu DNN7.6.5. |
| Software Dependencies | Yes | Our experiments were carried out on a single RTX 8000 GPU equipped with CUDA11.6 and Cu DNN7.6.5. |
| Experiment Setup | Yes | We set the learning rates to 0.1, implementing a decay at the 100th and 105th epochs out of a total of 110 epochs, using a decay factor of 0.1 as recommended by Pang et al. (2021). For optimization, we utilized stochastic gradient descent with a weight decay factor of 5e-4 and momentum set to 0.9. The upper bounds for adversarial perturbation were determined at 0.031 (ϵ = 8). The step size for generating adversarial examples for each model was set to one-fourth of the ℓ -bound of the respective model, over a span of 10 steps. For our method, the warm-up epoch was set to τ = 70 and the hyperparameter λ was set to λ = 1 for CIFAR10 and λ = 1.5 for CIFAR-100 and STL-10 due to the notably low performance of hard classes. |