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..
Mixture of GANs for Clustering
Authors: Yang Yu, Wen-Ji Zhou
IJCAI 2018 | Venue PDF | 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 EMAIL |
| 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 ο¬nally 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. |