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..
Learning Transferable Adversarial Examples via Ghost Networks
Authors: Yingwei Li, Song Bai, Yuyin Zhou, Cihang Xie, Zhishuai Zhang, Alan Yuille11458-11465
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we give a comprehensive experimental evaluation of the proposed Ghost Networks. |
| Researcher Affiliation | Academia | 1Johns Hopkins University 2University of Oxford |
| Pseudocode | No | The paper provides mathematical formulations and figures but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Li Yingwei/ ghost-network. ... We release source code and provide additional experimental results in https://github.com/Li Yingwei/ghost-network. |
| Open Datasets | Yes | We select 5000 images from the ILSVRC 2012 validation set... Imagenet: A large-scale hierarchical image database (Deng et al. 2009). The Neur IPS 2017 Adversarial Challenge also uses Image Net (Deng et al. 2009). |
| Dataset Splits | No | The paper uses pre-trained base models and selects 5000 images from the ILSVRC 2012 validation set for testing. It does not provide explicit training/validation/test splits for the models they develop or attack, as their method does not involve training new models from scratch. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with specific versions like PyTorch 1.x or Python 3.x). |
| Experiment Setup | Yes | If not specified otherwise, we follow the default settings in Kurakin, Goodfellow, and Bengio (2017a), i.e., step size α = 1 and the total iteration number N = min(ϵ + 4, 1.25ϵ). We set the maximum perturbation ϵ = 8 (the iteration number N = 10 in this case). For the momentum term, the decay factor μ is set to be 1 as in Dong et al. (2018). |