Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples
Authors: Dongyoon Yang, Insung Kong, Yongdai Kim
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments illustrate that our proposed algorithm improves the generalization (accuracy on examples) and robustness (accuracy on adversarial attacks) simultaneously to achieve the state-of-the-art performance. |
| Researcher Affiliation | Academia | 1Department of Statistics, Seoul National University, Seoul, Republic of Korea. |
| Pseudocode | Yes | Algorithm 1 Anti-Robust Weighted (ARo W) Regularization |
| Open Source Code | Yes | The code is available at https://github.com/dyoony/ARoW. |
| Open Datasets | Yes | In this section, we investigate ARo W algorithm in view of robustness and generalization by analyzing the four benchmark data sets CIFAR10, CIFAR100 (Krizhevsky, 2009) , F-MINST (Xiao et al., 2017) and SVHN dataset (Netzer et al., 2011). |
| Dataset Splits | No | The paper mentions 'training dataset' and 'test data' but does not explicitly specify distinct training/validation/test splits with percentages or counts, or refer to predefined validation splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of certain algorithms and optimizers (e.g., PGD, SGD) but does not specify any software libraries or dependencies with their version numbers (e.g., 'PyTorch 1.9', 'TensorFlow 2.x'). |
| Experiment Setup | Yes | For generating adversarial examples in the training phase, PGD10 with random initial, p = , ε = 8/255 and ν = 2/255 is used... For training prediction models, the SGD with momentum 0.9, weight decay 5 × 10−4, the initial learning rate of 0.1 and batch size of 128 is used and the learning rate is reduced by a factor of 10 at 60 and 90 epochs. Stochastic weighting average (SWA) (Izmailov et al., 2018) is employed after 50-epochs... Table 11 presents the hyperparameters used on our experiments. |