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..
UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator
Authors: Ziqi Pan, Li Niu, Liqing Zhang
NeurIPS 2022 | Venue PDF | 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 Arti๏ฌcial 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. |