f-GANs in an Information Geometric Nutshell
Authors: Richard Nock, Zac Cranko, Aditya K. Menon, Lizhen Qu, Robert C. Williamson
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide experiments1 that display the uplift that can be obtained through a principled design of the activation function (generator), or tuning of the link function (discriminator).The code used for our experiments is available through https://github.com/qulizhen/fgan_info_geometricSection 6 Experiments |
| Researcher Affiliation | Collaboration | Data61, the Australian National University and the University of Sydney {firstname.lastname, aditya.menon, bob.williamson}@data61.csiro.au |
| Pseudocode | No | The paper contains no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1The code used for our experiments is available through https://github.com/qulizhen/fgan_info_geometric |
| Open Datasets | Yes | Our datasets are MNIST [19] and LSUN tower category [38]. |
| Dataset Splits | No | The paper states the datasets used but does not explicitly provide specific train/validation/test dataset splits. It mentions a supplement (SM) provides the proof of the results in the main file and additional experiments which might contain this information, but it's not in the provided text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper mentions using same hyperparameters as for generators and Re LU activation for all hidden layer activation of generators, but does not provide specific hyperparameter values or detailed training configurations. |