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..
Multi-marginal Wasserstein GAN
Authors: Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on toy and real-world datasets demonstrate the effectiveness of MWGAN. |
| Researcher Affiliation | Academia | Jiezhang Cao , Langyuan Mo , Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan South China University of Technology, Peng Cheng Laboratory, The University of Adelaide EMAIL EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Multi-marginal WGAN. |
| Open Source Code | Yes | The source code of our method is available: https://github.com/caojiezhang/MWGAN. |
| Open Datasets | Yes | Datasets. We conduct experiments on three datasets. Note that all images are resized as 128 128. ... (ii) Celeb A [33] contains 202,599 face images, where each image has 40 binary attributes. ... (iii) Style painting [51]. |
| Dataset Splits | No | The paper does not explicitly provide the training, validation, and test splits for the main datasets (Toy, Celeb A, Style painting) used for the GAN model training. It only mentions a 90% training and 10% testing split for an auxiliary classifier trained on Celeb A. |
| Hardware Specification | Yes | All experiments are conducted based on Py Torch, with an NVIDIA TITAN X GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We use Adam [29] with β1=0.5 and β2=0.999 and set the learning rate as 0.0001. We train the model 100k iterations with batch size 16. We set α=10, τ=10 and Lf to be the number of target domains in Loss (7). |