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 [1].
Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Authors: Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on differentially private conditional image synthesis, since our focus is on generating high-dimensional data with privacy protection. We evaluate our method on both visual quality and data utility for downstream classification tasks. Additional experiments and analyses of the proposed semi-debiased Sinkhorn loss can be found in the Appendix. |
| Researcher Affiliation | Collaboration | Tianshi Cao1,2,4 Alex Bie3 Arash Vahdat4 Sanja Fidler1,2,4 Karsten Kreis4 1University of Toronto 2Vector Institute 3University of Waterloo 4NVIDIA |
| Pseudocode | Yes | Algorithm 1 DP-Sinkhorn |
| Open Source Code | No | Project page: https://nv-tlabs.github.io/DP-Sinkhorn. Code will be released through the project page4. |
| Open Datasets | Yes | Datasets We use 3 image datasets: MNIST [45], Fashion-MNIST [46], and Celeb A [47] downsampled to 32x32 pixels. |
| Dataset Splits | No | The paper does not explicitly state training, validation, and test dataset splits with percentages or sample counts. It mentions evaluation on a "synthetic dataset" and "real test sets" but not the splits for the original datasets. |
| Hardware Specification | Yes | In our experiments, DP-Sinkhorn can fit comfortably on an 11GB GPU, while GS-WGAN requires 24GB of GPU memory. |
| Software Dependencies | No | Our models are implemented in Py Torch. The paper mentions PyTorch but does not provide a specific version number or details for other software dependencies. |
| Experiment Setup | Yes | We set λ=0.05 for MNIST and Fashion-MNIST experiments, and λ=5 for Celeb A experiments. Complete implementation details can be found in the Appendix. |