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 [1].
Doubly Robust Instance-Reweighted Adversarial Training
Authors: Daouda Sow, Sen Lin, Zhangyang Wang, Yingbin Liang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on standard classification datasets demonstrate that our proposed approach outperforms related state-of-the-art baseline methods in terms of average robust performance, and at the same time improves the robustness against attacks on the weakest data points. 4 EXPERIMENTS |
| Researcher Affiliation | Academia | Daouda A. Sow Department of ECE The Ohio State University EMAIL Sen Lin Department of CS University of Houston EMAIL Zhangyang Wang Visual Informatics Group University of Texas at Austin EMAIL Yingbin Liang Department of ECE The Ohio State University EMAIL |
| Pseudocode | Yes | Algorithm 1 Compositional Implicit Differentiation (CID) |
| Open Source Code | Yes | Pytorch codes for our method are provided in the supplementary material of our submission. |
| Open Datasets | Yes | We consider image classification problems and compare the performance of the baselines on four datasets: CIFAR10 Krizhevsky & Hinton (2009), SVHN Netzer et al. (2011), STL10 Coates et al. (2011), and GTSRB Stallkamp et al. (2012). |
| Dataset Splits | Yes | All hyperparameters were fixed by holding out 10% of the training data as a validation set and selecting the values that achieve the best performance on the validation set. ...For CIFAR10, SVHN, and STL10 we use the training and test splits provided by Torchvision. |
| Hardware Specification | Yes | We run all baselines on a single NVIDIA Tesla V100 GPU. |
| Software Dependencies | Yes | All codes are tested with Python 3.7 and Pytorch 1.8. |
| Experiment Setup | Yes | More details about the training and hyperparameters search can be found in Appendix B. ...we train our baselines using stochastic gradient descent with a minibtach size of 128 and a momentum of 0.9. We use Res Net-18 as the backbone network as in Madry et al. (2017) and train our baselines for 60 epochs with a cyclic learning rate schedule where the maximum learning rate is set to 0.2 ...For the KL-divergence regularization parameter r in our algorithms, we use a decayed schedule where we initially set it to 10 and decay it to 1 and 0.1, respectively at epochs 40 and 50 (see fig. 2). |