# Stabilizing GAN Training with Multiple Random Projections

arXiv: https://arxiv.org/abs/1705.07831 · HTML: https://mlsanity.com/paper/1705.07831/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 15 of 15)

- [Projected GANs Converge Faster](https://mlsanity.com/paper/2111.01007/) (2021)
- [NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training](https://mlsanity.com/paper/2412.02030/) (2024)
- [SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks](https://mlsanity.com/paper/2204.00644/) (2022)
- [MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators](https://mlsanity.com/paper/2107.07260/) (2021)
- [D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles](https://mlsanity.com/paper/2202.05687/) (2022)
- [Enhanced Multimodal Representation Learning with Cross-modal KD](https://mlsanity.com/paper/2306.07646/) (2023)
- [Self-Supervised GANs with Label Augmentation](https://mlsanity.com/paper/2106.08601/) (2021)
- [Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network](https://mlsanity.com/paper/2105.02565/) (2021)
- [Differentially Private Generative Adversarial Networks with Model Inversion](https://mlsanity.com/paper/2201.03139/) (2022)
- [A Spectral Enabled GAN for Time Series Data Generation](https://mlsanity.com/paper/2103.01904/) (2021)
- [Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence](https://mlsanity.com/paper/2101.09225/) (2021)
- [Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets](https://mlsanity.com/paper/2302.00089/) (2023)
- [Training Generative Adversarial Networks with Adaptive Composite Gradient](https://mlsanity.com/paper/2111.05508/) (2021)
- [Stylized Projected GAN: A Novel Architecture for Fast and Realistic Image Generation](https://mlsanity.com/paper/2307.16275/) (2023)
- [EmbeddGAN: A Novel GAN Framework Using an Embedding Network and Gini Distance Correlation](https://mlsanity.com/paper/2609.22508/) (2026)

## Abstract (arXiv)

Training generative adversarial networks is unstable in high-dimensions as the true data distribution tends to be concentrated in a small fraction of the ambient space. The discriminator is then quickly able to classify nearly all generated samples as fake, leaving the generator without meaningful gradients and causing it to deteriorate after a point in training. In this work, we propose training a single generator simultaneously against an array of discriminators, each of which looks at a different random low-dimensional projection of the data. Individual discriminators, now provided with restricted views of the input, are unable to reject generated samples perfectly and continue to provide meaningful gradients to the generator throughout training. Meanwhile, the generator learns to produce samples consistent with the full data distribution to satisfy all discriminators simultaneously. We demonstrate the practical utility of this approach experimentally, and show that it is able to produce image samples with higher quality than traditional training with a single discriminator.