ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity
Authors: Kangkang Lu, Cuong Manh Nguyen, Xun Xu, Kiran Krishnamachari, Yu Jing Goh, Chuan-Sheng Foo
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS |
| Researcher Affiliation | Academia | 1 Institute for Infocomm Research, A*STAR, Singapore 2 National University of Singapore, Singapore |
| Pseudocode | Yes | Pseudocode detailing the training procedure is provided in Algorithm 1 in Appendix A.1. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate ARMOURED on the CIFAR-10 and SVHN datasets. We use the official train/test splits (50k/10k labeled samples) for CIFAR-10 (Krizhevsky et al., 2009) and reserve 5k samples from the training samples for a validation set. In our semi-supervised setup, the label budget is either 1k or 4k; remaining samples from training set are treated as unlabeled samples. For the SVHN dataset (Netzer et al., 2011), our train/validation/test split is 65,932 / 7,325 / 26,032 samples. |
| Dataset Splits | Yes | We use the official train/test splits (50k/10k labeled samples) for CIFAR-10 (Krizhevsky et al., 2009) and reserve 5k samples from the training samples for a validation set. In our semi-supervised setup, the label budget is either 1k or 4k; remaining samples from training set are treated as unlabeled samples. For the SVHN dataset (Netzer et al., 2011), our train/validation/test split is 65,932 / 7,325 / 26,032 samples. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running its experiments, only mentioning the use of a Wide ResNet backbone. |
| Software Dependencies | No | The paper mentions software components like 'batch normalization', 'leaky ReLU activation', and 'Adam optimizer', but it does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We train each method for 600 epochs on CIFAR-10-semi-4k and SVHN-semi-1k. Learning rate is decayed by a factor of 0.2 after the first 400k iterations. For the AT wrapper, we apply a 7-step PGD ℓ attack with total ϵ = 8/255 (for CIFAR-10), ϵ = 4/255 (for SVHN) and step size of ϵ/4. After tuning, we decide to apply (λDPP, λNEM) = (1, 1) for SVHN and (λDPP, λNEM) = (1, 0.5) for CIFAR-10. |