Graphical Generative Adversarial Networks
Authors: Chongxuan LI, Max Welling, Jun Zhu, Bo Zhang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 licx14@mails.tsinghua.edu.cn Max Welling M.Welling@uva.nl Jun Zhu dcszj@mail.tsinghua.edu.cn Bo Zhang dcszb@mail.tsinghua.edu.cn Department of Computer Science & Technology, Institute for Artiļ¬cial 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. |