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..
Toward Understanding Generative Data Augmentation
Authors: Chenyu Zheng, Guoqiang Wu, Chongxuan LI
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation results on the Gaussian mixture model and empirical results on generative adversarial nets support our theoretical conclusions. Our code is available at https://github.com/ML-GSAI/Understanding-GDA. |
| Researcher Affiliation | Academia | Chenyu Zheng1,2, Guoqiang Wu3, Chongxuan Li1,2 1 Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China 2 Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China 3 School of Software, Shandong University, Shandong, China |
| Pseudocode | No | The paper does not contain any explicit pseudocode blocks or algorithms. |
| Open Source Code | Yes | Our code is available at https://github.com/ML-GSAI/Understanding-GDA. |
| Open Datasets | Yes | In this part, we conduct experiments on the real CIFAR-10 dataset [52] with Res Nets [54] and various deep generative models, including conditional DCGAN (c DCGAN) [55], Style GAN2-ADA [56] and elucidating diffusion model (EDM) [30]. |
| Dataset Splits | No | The paper mentions training on a "train set" and evaluating on a "test set" but does not specify a validation set or explicit train/validation/test split percentages or counts. |
| Hardware Specification | Yes | All experiments are conducted on 8 NVIDIA GeForce RTX 3090 GPUs. |
| Software Dependencies | Yes | Our code is implemented with PyTorch [81] and Python 3.8.10. |
| Experiment Setup | Yes | Batch size is set to 128. We train 200 epochs for all classifiers. |