# Hierarchical Mixtures of Generators for Adversarial Learning

arXiv: https://arxiv.org/abs/1911.02069 · HTML: https://mlsanity.com/paper/1911.02069/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Multi-student Diffusion Distillation for Better One-step Generators](https://mlsanity.com/paper/2410.23274/) (2024)
- [Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface](https://mlsanity.com/paper/2206.08494/) (2022)
- [Enhanced Mixture 3D CGAN for Completion and Generation of 3D Objects](https://mlsanity.com/paper/2602.08046/) (2026)

## Abstract (arXiv)

Generative adversarial networks (GANs) are deep neural networks that allow us to sample from an arbitrary probability distribution without explicitly estimating the distribution. There is a generator that takes a latent vector as input and transforms it into a valid sample from the distribution. There is also a discriminator that is trained to discriminate such fake samples from true samples of the distribution; at the same time, the generator is trained to generate fakes that the discriminator cannot tell apart from the true samples. Instead of learning a global generator, a recent approach involves training multiple generators each responsible from one part of the distribution. In this work, we review such approaches and propose the hierarchical mixture of generators, inspired from the hierarchical mixture of experts model, that learns a tree structure implementing a hierarchical clustering with soft splits in the decision nodes and local generators in the leaves. Since the generators are combined softly, the whole model is continuous and can be trained using gradient-based optimization, just like the original GAN model. Our experiments on five image data sets, namely, MNIST, FashionMNIST, UTZap50K, Oxford Flowers, and CelebA, show that our proposed model generates samples of high quality and diversity in terms of popular GAN evaluation metrics. The learned hierarchical structure also leads to knowledge extraction.