# Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation

arXiv: https://arxiv.org/abs/2101.00990 · HTML: https://mlsanity.com/paper/2101.00990/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [Seeing What a GAN Cannot Generate](https://mlsanity.com/paper/1910.11626/) (2019)
- [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https://mlsanity.com/paper/1703.10717/) (2017)
- [Disentangled Person Image Generation](https://mlsanity.com/paper/1712.02621/) (2017)
- [Towards Diverse and Natural Image Descriptions via a Conditional GAN](https://mlsanity.com/paper/1703.06029/) (2017)
- [Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis](https://mlsanity.com/paper/1810.11610/) (2018)
- [Semi-parametric Image Synthesis](https://mlsanity.com/paper/1804.10992/) (2018)
- [Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation](https://mlsanity.com/paper/1908.00999/) (2019)
- [On Convergence and Stability of GANs](https://mlsanity.com/paper/1705.07215/) (2017)
- [KG-GAN: Knowledge-Guided Generative Adversarial Networks](https://mlsanity.com/paper/1905.12261/) (2019)

## Built on by (most cited first, 1 of 1)

- [Flow Plugin Network for conditional generation](https://mlsanity.com/paper/2110.04081/) (2021)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) are an arrange of two neural networks -- the generator and the discriminator -- that are jointly trained to generate artificial data, such as images, from random inputs. The quality of these generated images has recently reached such levels that can often lead both machines and humans into mistaking fake for real examples. However, the process performed by the generator of the GAN has some limitations when we want to condition the network to generate images from subcategories of a specific class. Some recent approaches tackle this textitconditional generation by introducing extra information prior to the training process, such as image semantic segmentation or textual descriptions. While successful, these techniques still require defining beforehand the desired subcategories and collecting large labeled image datasets representing them to train the GAN from scratch. In this paper we present a novel and alternative method for guiding generic non-conditional GANs to behave as conditional GANs. Instead of re-training the GAN, our approach adds into the mix an encoder network to generate the high-dimensional random input vectors that are fed to the generator network of a non-conditional GAN to make it generate images from a specific subcategory. In our experiments, when compared to training a conditional GAN from scratch, our guided GAN is able to generate artificial images of perceived quality comparable to that of non-conditional GANs after training the encoder on just a few hundreds of images, which substantially accelerates the process and enables adding new subcategories seamlessly.