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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Authors: Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we provide the first and comprehensive study on the adversarial training recipe of Vi Ts via extensive evaluation of various training techniques across benchmark datasets. |
| Researcher Affiliation | Academia | 1 Key Lab. of Machine Perception (Mo E), School of Intelligence Science and Technology, Peking University 2 The University of Tokyo 3 School of Mathematical Sciences, Peking University 4 Independent Researcher 5 Institute for Artificial Intelligence, Peking University |
| Pseudocode | Yes | The details of ARD and PRM based adversarial training for Vi Ts are summarized in Appendix C. |
| Open Source Code | Yes | Our code is available at https://github.com/mo666666/When-Adversarial-Training-Meets-Vision-Transformers. |
| Open Datasets | Yes | Here, we use datasets of CIFAR-10 [36] and Imagenette [37] (a subset of 10 classes from Image Net-1K). |
| Dataset Splits | Yes | Note that the latest version of Imagenette (imagenette-v22) reshuffles the sampled subset of Image Net-1K and then splits the training and validation set. |
| Hardware Specification | No | The paper states it provides information on compute resources in Section 3, but Section 3 only mentions training settings and datasets, not specific hardware details like GPU/CPU models or types of clusters. For example, it does not specify 'type of GPUs' as indicated in the checklist. |
| Software Dependencies | No | The paper mentions 'pytorch-image-models' in a footnote, suggesting the use of PyTorch, but it does not specify version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | All models (unless otherwise specified) are pre-trained on Image Net-1K and are adversarially trained for 40 epochs using SGD with weight decay 1e-4, and an initial learning rate 0.1 that is divided by 10 at the 36-th and 38-th epoch. Simple data augmentations such as random crop with padding and random horizontal flip are applied. During adversarial training, we use PGD-10 with step size 2/255 to craft adversarial examples. |