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..
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
Authors: Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments are conducted in six image classification datasets, consisting of three natural object datasets and three medical datasets. |
| Researcher Affiliation | Collaboration | 1 College of Computer Science and Technology, Zhejiang University, Hangzhou, China 2 Huawei Noah s Ark Lab, Beijing, China 3 Shanghai Institute for Advanced Study of Zhejiang University, Shanghai, China 4 Shanghai AI Laboratory, Shanghai, China 5 Key Laboratory for Corneal Diseases Research of Zhejiang Province, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1: Minibatch training of Confounder GAN |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] We provide instructions needed to reproduce our experimental results in Appendix G. |
| Open Datasets | Yes | We select three natural object datasets and three medical datasets for algorithm evaluation, including SVHN [22], CIFAR10 [16], Image Net25 [5], Blood MNIST [45], Keratitis and ISIC [1]. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix G. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix G. |
| Software Dependencies | No | No specific version numbers for software dependencies were mentioned in the main text. Appendix G is referenced for more experimental details, but the self-assessment only confirms training details, not specific software versions. |
| Experiment Setup | Yes | Referring to Huang et al. [12], the maximum perturbation is set to 8/255 in SVHN and CIFAR10, and 16/255 in other datasets, thus ensuring it is imperceptible to human observers. These settings are fixed for all experiments, unless otherwise explicitly stated. See Appendix G for more experimental details. |