MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators
Authors: Jinyoung Choi, Bohyung Han
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {jin0.choi,bhhan}@snu.ac.kr |
| 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 first 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. |