ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
Authors: Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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. |