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..
Towards a Better Global Loss Landscape of GANs
Authors: Ruoyu Sun, Tiantian Fang, Alexander Schwing
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data show that the predicted bad basin can indeed appear in training. We also perform experiments to support our theory that Rp GAN has a better landscape than separable-GAN. For instance, we empirically show that Rp GAN performs better than separable-GAN with relatively narrow neural nets. |
| Researcher Affiliation | Academia | Ruoyu Sun , Tiantian Fang, Alex Schwing University of Illinois at Urbana-Champaign ruoyus,tf6,EMAIL |
| Pseudocode | No | The paper does not contain any blocks explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | The code is available at https://github.com/Ailsa F/RS-GAN. |
| Open Datasets | Yes | For setting (A), we test on CIFAR-10 and STL-10 data. |
| Dataset Splits | No | The paper refers to using CIFAR-10 and STL-10, which have standard splits, but it does not explicitly state the train/validation/test percentages or sample counts for these datasets. It mentions evaluating generated samples (50k and 10k) but not the dataset splits themselves. |
| Hardware Specification | No | The paper does not specify any particular hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify software dependencies like programming language versions or library versions (e.g., PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | Yes | For the optimizer, we use Adam with the discriminator s learning rate 0.0002. For CIFAR-10 on Res Net, we set β1 = 0 and β2 = 0.9 in Adam; for others, β1 = 0.5 and β2 = 0.999. We tune the generator s learning rate and run 100k iterations in total. |