DAFA: Distance-Aware Fair Adversarial Training
Authors: Hyungyu Lee, Saehyung Lee, Hyemi Jang, Junsung Park, Ho Bae, Sungroh Yoon
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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. |