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..
MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators
Authors: Jinyoung Choi, Bohyung Han
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our algorithm using multiple evaluation metrics in the standard datasets for diverse tasks. We evaluate the performance of MCL-GAN on unconditional and conditional image generation. |
| Researcher Affiliation | Academia | Jinyoung Choi1,3 Bohyung Han1,2,3 1ECE, 2IPAI, 3ASRI Seoul National University, Korea EMAIL |
| Pseudocode | No | The paper does not contain a pseudocode block or algorithm block. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Appendix C and D, and the supplementary material. |
| Open Datasets | Yes | We run the unconditional GAN experiment on four distinct datasets including MNIST [39], Fashion MNIST [40], CIFAR-10 [41] and Celeb A [42]. |
| Dataset Splits | Yes | For the Style GAN2 experiments on Celeb A, we use the ο¬rst and last 30K images from the align&cropped version of the train and validation splits following [30]. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 2 and Appendix C. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix A.3. |
| Software Dependencies | No | The paper mentions applying the method to different GAN architectures (DCGAN, StyleGAN2) but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | 5.2.1 Experiment setup and evaluation protocol. Appendix C describes more details of our setting. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 2 and Appendix C. |