Parallel Wasserstein Generative Adversarial Nets with Multiple Discriminators
Authors: Yuxin Su, Shenglin Zhao, Xixian Chen, Irwin King, Michael Lyu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experimental part, we evaluate the proposed parallel approach on two widely used image datasets CIFAR-10 and LSUN [Yu et al., 2015]. |
| Researcher Affiliation | Collaboration | 1The Chinese University of Hong Kong, Shatin, Hong Kong 2Youtu Lab, Tencent, Shenzhen, China |
| Pseudocode | Yes | Algorithm 1: Generator as Master Unit |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | In the experimental part, we evaluate the proposed parallel approach on two widely used image datasets CIFAR-10 and LSUN [Yu et al., 2015]. |
| Dataset Splits | No | The paper mentions using datasets like CIFAR-10 and LSUN but does not explicitly provide specific training, validation, and test dataset splits needed for reproduction. |
| Hardware Specification | Yes | All experiments in this section are conducted in a cluster with four machines with 2 NVIDIA GTX 1080 GPUs each. |
| Software Dependencies | No | The paper states 'We implement our algorithms with Py Torch [Paszke et al., 2017]', but it does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We assign the number of iteration for per mini-batch update T = 5 for all experiments. |