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..
Parallel Wasserstein Generative Adversarial Nets with Multiple Discriminators
Authors: Yuxin Su, Shenglin Zhao, Xixian Chen, Irwin King, Michael Lyu
IJCAI 2019 | Venue PDF | 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. |