A Unified View of cGANs with and without Classifiers
Authors: Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct our experiments on CIFAR-10 [20] and Tiny Image Net [22] for analysis, and Image Net [6] for large-scale empirical study. ... In our experiment, we use two common metrics, Frechet Inception Distance [FID; 14] and Inception Score [IS; 44], to evaluate our generation quality and diversity. |
| Researcher Affiliation | Collaboration | Si-An Chen National Taiwan University d09922007@csie.ntu.edu.tw Chun-Liang Li Google Cloud AI chunliang@google.com Hsuan-Tien Lin National Taiwan University htlin@ntu.edu.tw |
| Pseudocode | No | The overall training procedure of ECGAN is presented in Appendix E. This appendix describes the procedure in text, not a formal pseudocode block. |
| Open Source Code | Yes | The code is available at https://github.com/sian-chen/Py Torch-ECGAN. |
| Open Datasets | Yes | We conduct our experiments on CIFAR-10 [20] and Tiny Image Net [22] for analysis, and Image Net [6] for large-scale empirical study. All datasets are publicly available for research use. |
| Dataset Splits | No | The paper provides training and test set sizes in Table 2 but does not explicitly detail a validation set split or its size. |
| Hardware Specification | Yes | The experiments take 1-2 days on single GPU (Nvidia Tesla V100) machines for CIFAR-10, Tiny Image Net, and take 6 days on 8-GPU machines for Image Net. |
| Software Dependencies | No | We use Studio GAN [16] to conduct our experiments. Studio GAN is a Py Torch-based project distributed under the MIT license... The code is available at https://github.com/sian-chen/Py Torch-ECGAN. No specific version numbers for PyTorch or other dependencies are mentioned. |
| Experiment Setup | Yes | We fix the learning rate for generators and discriminators to 0.0001 and 0.0004, respectively, and tune λclf in {1, 0.1, 0.05, 0.01}. We follow the setting λc = 1 in [16] when using 2C loss, and set α = 1 when applying unconditional GAN loss. |