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..
Instability and Local Minima in GAN Training with Kernel Discriminators
Authors: Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical simulations are provided that predictably replicate these behaviors. We conduct a simple experiment on low-dimensional, synthetic data to illustrate the behavior that the theory predicts. We also look at the frequency of GAN failure modes as a function of kernel width of the discriminator. Our main observation is that failure most often occurs when the model is in an isolated points regime at small kernel width. |
| Researcher Affiliation | Academia | Evan Becker Dept. CS UCLA EMAIL Parthe Pandit HDSI UC, San Diego EMAIL Sundeep Rangan Dept. ECE NYU EMAIL Alyson K. Fletcher Dept. Statistics UCLA EMAIL |
| Pseudocode | No | The paper includes mathematical equations and descriptions of dynamics, but it does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is added as supplementary material |
| Open Datasets | No | Two simple datasets for the true data are used: A set of Nr = 4 points arranged on uniformly on the unit circle in dimension d = 2; and a set of Nr = 10 points randomly distributed on the unit sphere in dimension d = 10. In both cases, we initialize Ng = Nr generated point as Gaussians with zero mean and E exj 2 = 1. We approximate the RBF discriminator using a random Fourier feature map as in [24]. |
| Dataset Splits | No | The paper mentions '40000 training steps' and '100 trials', but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | We only used standard existing code packages such as pytorch. The paper mentions 'pytorch' and 'random Fourier feature map' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | We set λ = 0.01 and ηd = ηg = 10 3 and use 40000 training steps. Other details are in the Supplementary material. |