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..
Graphical Generative Adversarial Networks
Authors: Chongxuan LI, Max Welling, Jun Zhu, Bo Zhang
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement our model using the Tensor Flow [1] library. In all experiments, we optimize the JS-divergence. We use the widely adopted DCGAN architecture [33] in all experiments to fairly compare Graphical-GAN with existing methods. We evaluate GMGAN on the MNIST [20], SVHN [30], CIFAR10 [19] and Celeb A [23] datasets. We evaluate SSGAN on the Moving MNIST [38] and 3D chairs [3] datasets. Qualitatively, Graphical-GAN can infer the latent structures and generate structured samples without any regularization, which is required by existing models [4, 43, 6, 42]; Quantitatively, Graphical-GAN can outperform all baseline methods [7 9] in terms of inference accuracy, sample quality and reconstruction error consistently and substantially. |
| Researcher Affiliation | Academia | Chongxuan Li EMAIL Max Welling EMAIL Jun Zhu EMAIL Bo Zhang EMAIL Department of Computer Science & Technology, Institute for Arti๏ฌcial Intelligence, BNRist Center, THBI Lab, State Key Lab for Intell. Tech. & Sys., Tsinghua University. Correspondence to: J. Zhu. University of Amsterdam, and the Canadian Institute for Advanced Research (CIFAR). |
| Pseudocode | Yes | Algorithm 1 Local algorithm for Graphical-GAN |
| Open Source Code | Yes | Our source code is available at https://github.com/zhenxuan00/graphical-gan. |
| Open Datasets | Yes | We evaluate GMGAN on the MNIST [20], SVHN [30], CIFAR10 [19] and Celeb A [23] datasets. We evaluate SSGAN on the Moving MNIST [38] and 3D chairs [3] datasets. |
| Dataset Splits | No | The paper mentions 'test samples' when discussing evaluation but does not provide specific details on the percentages or counts for training, validation, and test splits, nor does it cite a specific split methodology used for these standard datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications, or cloud instance types) used for running the experiments. It only mentions the use of the Tensor Flow library. |
| Software Dependencies | No | The paper mentions using the 'Tensor Flow [1] library' but does not provide a specific version number for TensorFlow or any other software dependencies. |
| Experiment Setup | No | The paper states that 'In all experiments, we optimize the JS-divergence. We use the widely adopted DCGAN architecture [33] in all experiments' but does not provide specific hyperparameters such as learning rates, batch sizes, number of epochs, or detailed optimizer settings in the main text. |