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..
Disconnected Manifold Learning for Generative Adversarial Networks
Authors: Mahyar Khayatkhoei, Maneesh K. Singh, Ahmed Elgammal
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct several experiments to illustrate the aforementioned shortcoming of GANs, its consequences in practice, and the effectiveness of our proposed modifications in alleviating these issues. |
| Researcher Affiliation | Collaboration | Mahyar Khayatkhoei Department of Computer Science Rutgers University EMAIL Ahmed Elgammal Department of Computer Science Rutgers University EMAIL Maneesh Singh Verisk Analytics EMAIL |
| Pseudocode | Yes | See Appendix A for details of our algorithm and the DMGAN objectives. (Appendix A contains Algorithm 1 Training DMWGAN) |
| Open Source Code | No | The paper does not contain any explicit statement or link providing access to the source code for the described methodology. |
| Open Datasets | Yes | MNIST [16] is particularly suitable since samples with different class labels can be reasonably interpreted as lying on disjoint manifolds... We combine 20K face images from Celeb A dataset [17] and 20K bedroom images from LSUN Bedrooms dataset [27] to construct a natural image dataset supported on a disconnected manifold. |
| Dataset Splits | No | The paper does not explicitly provide information about training, validation, and test dataset splits. |
| Hardware Specification | No | The paper describes network architectures and training parameters but does not specify the hardware (e.g., GPU/CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Adam optimizer and DCGAN-like networks but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | In all experiments, we train each model for a total of 200 epochs with a five to one update ratio between discriminator and generator... See Appendix B for details of our networks and the hyperparameters. (Appendix B states: We use Adam optimizer with β1 = 0 and β2 = 0.9 for both generator and discriminator. Learning rate for generator and discriminator is 1e-4, and for Q and prior is 1e-5. We also use a learning rate decay of 0.5 per 10000 iterations for the prior training. We use batch size of 64 for all experiments. We use 20 generators for MNIST and 5 for Face-Bed, unless otherwise stated. We train all models for 200 epochs.) |