Instability and Local Minima in GAN Training with Kernel Discriminators
Authors: Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 evbecker@cs.ucla.edu Parthe Pandit HDSI UC, San Diego parthepandit@ucsd.edu Sundeep Rangan Dept. ECE NYU srangan@nyu.edu Alyson K. Fletcher Dept. Statistics UCLA akfletcher@ucla.edu |
| 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. |