UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator
Authors: Ziqi Pan, Li Niu, Liqing Zhang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results verify the effectiveness of our Uni GAN in learning a uniform generator and improving uniform diversity. |
| Researcher Affiliation | Academia | Ziqi Pan, Li Niu , Liqing Zhang Mo E Key Lab of Artiļ¬cial Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University, Shanghai, China |
| Pseudocode | No | The paper describes its methodology in text and equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We include code in the supplementary material, including the implementation of our NF-based generator, the LT technique, the generator uniformity regularization, and the udiv metric. |
| Open Datasets | Yes | We also provide results on simple datasets including MNIST [58], Fashion MNIST [59] and their colored version [22], and CIFAR10 [60]. We also provide results on natural image datasets including Celeb A [61], FFHQ [62], AFHQ [63] and LSUN [64]. |
| Dataset Splits | No | The paper states that training details, which would include data splits, are provided in the supplementary material, not in the main text: 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See supplementary.' |
| Hardware Specification | No | The paper states that details about the total amount of compute and type of resources used are provided in the supplementary material, not in the main text: 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See supplementary.' |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., libraries, frameworks, or operating systems) used in the experiments within the main text. |
| Experiment Setup | No | The paper indicates that 'all the training details (e.g., data splits, hyperparameters, how they were chosen)' are specified in the supplementary material, not in the main text. |