ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS
Authors: Chao GAO, jiyi LIU, Yuan YAO, Weizhi ZHU
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments in Section 5 are provided to show the success of these GANs. |
| Researcher Affiliation | Academia | Chao Gao Department of Statistics University of Chicago Chicago, IL 60637 USA chaogao@galton.uchicago.edu; Jiyi Liu Department of Statistics and Data Science Yale University New Haven, CT 06511 USA jiyi.liu@yale.edu; Yuan Yao & Weizhi Zhu Department of Mathematics Hong Kong University of Science and Technology Kowloon, Hong Kong yuany@ust.hk; wzhuai@connect.ust.hk |
| Pseudocode | Yes | Algorithm 1 JS-GAN: argminη maxw[ 1/n Pn i=1 log Dw(Xi) + E log(1 Dw(Gη(Z)))] |
| Open Source Code | Yes | A PyTorch implementation is available at https://github.com/zhuwzh/Robust-GAN-Center. |
| Open Datasets | No | The paper utilizes synthetically generated data (e.g., from (1 ϵ)N(0p, Ip) + ϵQ) and describes the data generation process rather than using or providing a publicly available dataset. |
| Dataset Splits | No | The paper primarily uses synthetically generated data and does not specify traditional train/validation/test splits for publicly available datasets. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch implementation' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Table 5: Choices of hyper-parameters. The parameter λ is the penalty factor for the regularization term (17) and other parameters are listed in Algorithm 1. We apply Xavier initialization (Glorot & Bengio, 2010) for both JS-GAN and TV-GAN trainings. |