# cGANs with Multi-Hinge Loss

arXiv: https://arxiv.org/abs/1912.04216 · HTML: https://mlsanity.com/paper/1912.04216/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [Improving GANs for Long-Tailed Data through Group Spectral Regularization](https://mlsanity.com/paper/2208.09932/) (2022)
- [Efficient Conditional GAN Transfer with Knowledge Propagation across Classes](https://mlsanity.com/paper/2102.06696/) (2021)
- [The Nuts and Bolts of Adopting Transformer in GANs](https://mlsanity.com/paper/2110.13107/) (2021)
- [CoopInit: Initializing Generative Adversarial Networks via Cooperative Learning](https://mlsanity.com/paper/2303.11649/) (2023)
- [Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data](https://mlsanity.com/paper/2102.10485/) (2021)
- [A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention](https://mlsanity.com/paper/2106.09914/) (2021)
- [Physical Context and Timing Aware Sequence Generating GANs](https://mlsanity.com/paper/2110.04077/) (2021)
- [Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks](https://mlsanity.com/paper/2207.12315/) (2022)

## Abstract (arXiv)

We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings. We study the compromise between training a state of the art generator and an accurate classifier simultaneously, and propose a way to use our algorithm to measure the degree to which a generator and critic are class conditional. We show the trade-off between a generator-critic pair respecting class conditioning inputs and generating the highest quality images. With our multi-hinge loss modification we are able to improve Inception Scores and Frechet Inception Distance on the Imagenet dataset. We make our tensorflow code available at https://github.com/ilyakava/gan.