Toward Understanding Generative Data Augmentation
Authors: Chenyu Zheng, Guoqiang Wu, Chongxuan LI
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |