Mixture of GANs for Clustering
Authors: Yang Yu, Wen-Ji Zhou
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments show that the proposed GANMM can have good performance on complex data as well as simple data. |
| Researcher Affiliation | Academia | Yang Yu and Wen-Ji Zhou National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China {yuy,zhouwj}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 GAN mixture model learning algorithm |
| Open Source Code | Yes | An implementation of GANMM can be found at https://github.com/eyounx/GANMM. |
| Open Datasets | Yes | On MNIST Dataset [Le Cun et al., 1998]. It is a handwriting digital dataset containing 60,000 images of size 28 by 28 pixels consist of 10 classes from digit 0 to 9 ." and "We finally compare the clustering performance on two UCI datasets [Dua and Karra, 2017]. |
| Dataset Splits | No | The paper does not provide specific training, validation, and test dataset splits with percentages or counts, nor does it refer to standard predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or processing units) used for running its experiments. |
| Software Dependencies | No | The paper mentions using Wasserstein GAN and DEC implementations from GitHub but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper describes network architectures (e.g., 'two convolution layers and two dense layers') and parameters in Algorithm 1 (e.g., 'learning rate', 'number of epoch for GANs'), but does not provide specific numerical values for these hyperparameters or other system-level training settings. |