# Class-Splitting Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1709.07359 · HTML: https://mlsanity.com/paper/1709.07359/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Instance-Conditioned GAN](https://mlsanity.com/paper/2109.05070/) (2021)
- [MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation](https://mlsanity.com/paper/1805.02481/) (2018)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) produce systematically better quality samples when class label information is provided., i.e. in the conditional GAN setup. This is still observed for the recently proposed Wasserstein GAN formulation which stabilized adversarial training and allows considering high capacity network architectures such as ResNet. In this work we show how to boost conditional GAN by augmenting available class labels. The new classes come from clustering in the representation space learned by the same GAN model. The proposed strategy is also feasible when no class information is available, i.e. in the unsupervised setup. Our generated samples reach state-of-the-art Inception scores for CIFAR-10 and STL-10 datasets in both supervised and unsupervised setup.