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..
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation
Authors: Yuzheng Wang, Zhaoyu Chen, Dingkang Yang, Pinxue Guo, Kaixun Jiang, Wenqiang Zhang, Lizhe Qi
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed DFARD method on 32 32 CIFAR datasets (Krizhevsky, Hinton et al. 2009)... The robustness performances of our and other baseline methods are shown in Table 1. |
| Researcher Affiliation | Academia | 1Shanghai Engineering Research Center of AI & Robotics, Academy for Engineering & Technology, Fudan University 2Engineering Research Center of AI & Robotics, Ministry of Education, Academy for Engineering & Technology, Fudan University EMAIL |
| Pseudocode | Yes | Algorithm 1: Training process of our Data-Free Adversarial Robustness Distillation |
| Open Source Code | No | No explicit statement about providing open-source code for the described methodology or a direct link to a code repository was found. |
| Open Datasets | Yes | We evaluate the proposed DFARD method on 32 32 CIFAR datasets (Krizhevsky, Hinton et al. 2009) |
| Dataset Splits | No | The paper uses CIFAR datasets which have standard splits, but it does not explicitly state the train/validation/test split percentages, sample counts, or refer to predefined splits with specific details for reproducibility in its own experiments. |
| Hardware Specification | Yes | All models are trained on RTX 3090 GPUs (Paszke et al. 2019). |
| Software Dependencies | No | The paper states, 'Our proposed method and all others are implemented in Py Torch,' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The students are trained via SGD optimizer with cosine annealing learning rate with an initial value of 0.05, momentum of 0.9, and weight decay of 1e-4. The generators are trained via Adam optimizer with a learning rate of 1e-3, β1 of 0.5, β2 of 0.999. The distillation batch size and the synthesis batch size are both 256. The distillation epochs T is 200, the iterations of generator Tg is 1, and the iterations of student Ts is 5. Both the student model and the generator are randomly initialized. A 10-step PGD (PGD-10) with a random start size of 0.001 and step size of 2/255 is used to generate adversarial samples. The perturbation bounds are set to L norm ϵ = 8/255. The perturbation steps for PGDS, PGDT and CW are 20. |