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..
A Unified View of cGANs with and without Classifiers
Authors: Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
NeurIPS 2021 | Venue PDF | 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 EMAIL Chun-Liang Li Google Cloud AI EMAIL Hsuan-Tien Lin National Taiwan University EMAIL |
| 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. |