Lipschitz Generative Adversarial Nets
Authors: Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically study the gradient uninformativeness problem and the performance of various objectives of Lipschitz GANs. The results in terms of Inception Score (IS) (Salimans et al., 2016) and Frechet Inception Distance (FID) (Heusel et al., 2017) are presented in Table 2. |
| Researcher Affiliation | Academia | 1Shanghai Jiao Tong University 2Peking University 3Stanford University. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The anonymous code is provided in the supplemental material. |
| Open Datasets | Yes | We use the CIFAR-10 training set. Objective CIFAR-10 Tiny Image Net IS FID IS FID x 7.68 0.03 18.35 0.12 8.66 0.04 16.47 0.04 |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For all experiments, we adopt the network structures and hyper-parameter setting from (Gulrajani et al., 2017), where WGAN-GP in our implementation achieves IS 7.71 0.03 and FID 18.86 0.13 on CIFAR-10. We use Max GP for all experiments and search the best λ in [0.01, 0.1, 1.0, 10.0]. We use 200,000 iterations for better convergence and use 500k samples to evaluate IS and FID for preferable stability. |