# Data Dieting in GAN Training

arXiv: https://arxiv.org/abs/2004.04642 · HTML: https://mlsanity.com/paper/2004.04642/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [StyleGAN-Human: A Data-Centric Odyssey of Human Generation](https://mlsanity.com/paper/2204.11823/) (2022)
- [Signal Propagation in a Gradient-Based and Evolutionary Learning System](https://mlsanity.com/paper/2102.08929/) (2021)
- [Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/2106.13590/) (2021)
- [Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs](https://mlsanity.com/paper/2607.01907/) (2026)

## Abstract (arXiv)

We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resource requirements, e.g. time and memory. We ask how much data reduction impacts generator performance and gauge the additive value of generator ensembles. In addition to considering stand-alone GAN training and ensembles of generator models, we also consider reduced data training on an evolutionary GAN training framework named Redux-Lipizzaner. Redux-Lipizzaner makes GAN training more robust and accurate by exploiting overlapping neighborhood-based training on a spatial 2D grid. We conduct empirical experiments on Redux-Lipizzaner using the MNIST and CelebA data sets.