Robust Local Features for Improving the Generalization of Adversarial Training
Authors: Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, John E. Hopcroft
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on STL-10, CIFAR-10 and CIFAR-100 show that RLFAT significantly improves both the adversarially robust generalization and the standard generalization of adversarial training. |
| Researcher Affiliation | Academia | Chuanbiao Song & Kun He & Jiadong Lin School of Computer Science and Technology Huazhong University of Science and Technology Wuhan, 430074, China {cbsong,brooklet60,jdlin}@hust.edu.cn Liwei Wang School of Electronics Engineering and Computer Sciences, Peking University Peking, China wanglw@cis.pku.edu.cn John E. Hopcroft Department of Computer Science Cornell University, NY 14853, USA jeh@cs.cornell.edu |
| Pseudocode | Yes | Algorithm 1 Robust Local Features for Adversarial Training (RLFAT). |
| Open Source Code | Yes | Codes are available online1. 1https://github.com/JHL-HUST/RLFAT |
| Open Datasets | Yes | Datasets. We compare the proposed methods with the baselines on widely used benchmark datasets, namely CIFAR-10 and CIFAR-100 (Krizhevsky & Hinton, 2009). Since adversarially robust generalization becomes increasingly hard for high dimensional data and little training data (Schmidt et al., 2018), we also consider one challenging dataset: STL-10 (Coates et al.), which contains 5, 000 training images, with 96 96 pixels per image. |
| Dataset Splits | No | The paper mentions training data (e.g., "5,000 training images" for STL-10) but does not explicitly state validation dataset splits or specific numbers for validation sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not specify version numbers for any software libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | For all training jobs, we use the Adam optimizer with a learning rate of 0.001 and a batch size of 32. For CIFAR-10 and CIFAR-100, we run 79,800 steps for training. For STL-10, we run 29,700 steps for training. For STL-10 and CIFAR-100, the adversarial examples are generated with step size 0.0075, 7 iterations, and ϵ = 0.03. For CIFAR-10, the adversarial examples are generated with step size 0.0075, 10 iterations, and ϵ = 0.03. |