# GANSpace: Discovering Interpretable GAN Controls

**Authors:** Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris  
**arXiv:** [2004.02546](https://arxiv.org/abs/2004.02546)  
**Date:** 2020-04-06  
**Summary page:** https://www.summarizepaper.com/en/paper/2004.02546v1/

## Key points

- GANSpace: Discovering Interpretable GAN Controls is a paper authored by Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris.
- The paper introduces a technique for analyzing and controlling Generative Adversarial Networks (GANs) in image synthesis.
- Principal Components Analysis (PCA) is used to identify important latent directions within the GANs and extract meaningful information from its internal representations.
- This analysis helps users understand the factors contributing to image generation.
- The authors propose a method for defining interpretable edits through layer-wise application of these identified edit directions.
- Users can make specific changes to different layers of the GAN model and achieve desired modifications in generated images.
- The technique can be applied to BigGAN in a similar manner as StyleGAN, expanding its applicability beyond specific GAN architectures.
- Numerous examples and results are showcased, demonstrating how the technique enables users to discover interpretable controls for image synthesis.
- The approach provides a simple yet effective way to analyze GANs and create interpretable controls for image synthesis using PCA and activation space analysis.

## Summary

GANSpace: Discovering Interpretable GAN Controls is a paper authored by Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris. The paper introduces a technique for analyzing and controlling Generative Adversarial Networks (GANs) in image synthesis. By leveraging Principal Components Analysis (PCA) in activation space, the authors are able to identify important latent directions within the GANs and extract meaningful information from its internal representations. This analysis helps users understand the underlying factors that contribute to image generation. Based on this analysis, the authors propose a method for defining interpretable edits through layer-wise application of these identified edit directions. This approach allows users to make specific changes to different layers of the GAN model and achieve desired modifications in the generated images. Additionally, the paper demonstrates that their technique can be applied to BigGAN in a similar manner as StyleGAN, expanding its applicability beyond specific GAN architectures. The authors showcase numerous examples and results obtained from applying their method on GANs trained on various datasets. These examples highlight how their technique enables users to discover a large number of interpretable controls for image synthesis. In summary,  provides a simple yet effective approach for analyzing GANs and creating interpretable controls for image synthesis using  and . The demonstrated results on diverse datasets validate the effectiveness and versatility of this technique in generating realistic and controllable images with .