# Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning

arXiv: https://arxiv.org/abs/2102.12139 · HTML: https://mlsanity.com/paper/2102.12139/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 20 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [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)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Towards Principled Methods for Training Generative Adversarial Networks](https://mlsanity.com/paper/1701.04862/) (2017)
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)
- [Unrolled Generative Adversarial Networks](https://mlsanity.com/paper/1611.02163/) (2016)
- [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https://mlsanity.com/paper/1703.10717/) (2017)
- [Invertible Conditional GANs for image editing](https://mlsanity.com/paper/1611.06355/) (2016)
- [Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?](https://mlsanity.com/paper/1810.09102/) (2018)
- [Face Aging With Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1702.01983/) (2017)
- [Towards the Automatic Anime Characters Creation with Generative Adversarial Networks](https://mlsanity.com/paper/1708.05509/) (2017)

## Built on by (most cited first, 4 of 4)

- [Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation](https://mlsanity.com/paper/2307.13978/) (2023)
- [Measuring Feature Sparsity in Language Models](https://mlsanity.com/paper/2310.07837/) (2023)
- [Exploring and Leveraging Class Vectors for Classifier Editing](https://mlsanity.com/paper/2510.11268/) (2025)
- [U-Face: An Efficient and Generalizable Framework for Unsupervised Facial Attribute Editing via Subspace Learning](https://mlsanity.com/paper/2603.14004/) (2026)

## Abstract (arXiv)

With great progress in the development of Generative Adversarial Networks (GANs), in recent years, the quest for insights in understanding and manipulating the latent space of GAN has gained more and more attention due to its wide range of applications. While most of the researches on this task have focused on unsupervised learning method, which induces difficulties in training and limitation in results, our work approaches another direction, encoding human's prior knowledge to discover more about the hidden space of GAN. With this supervised manner, we produce promising results, demonstrated by accurate manipulation of generated images. Even though our model is more suitable for task-specific problems, we hope that its ease in implementation, preciseness, robustness, and the allowance of richer set of properties (compared to other approaches) for image manipulation can enhance the result of many current applications.