# SWAGAN: A Style-based Wavelet-driven Generative Model

arXiv: https://arxiv.org/abs/2102.06108 · HTML: https://mlsanity.com/paper/2102.06108/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 27 of 27)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?](https://mlsanity.com/paper/1904.03189/) (2019)
- [Designing an Encoder for StyleGAN Image Manipulation](https://mlsanity.com/paper/2102.02766/) (2021)
- [Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions](https://mlsanity.com/paper/2003.01826/) (2020)
- [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https://mlsanity.com/paper/1506.05751/) (2015)
- [Photorealistic Style Transfer via Wavelet Transforms](https://mlsanity.com/paper/1903.09760/) (2019)
- [Fourier Spectrum Discrepancies in Deep Network Generated Images](https://mlsanity.com/paper/1911.06465/) (2019)
- [A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction](https://mlsanity.com/paper/1610.09736/) (2016)
- [Multi-level Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1907.03128/) (2019)
- [Wavelet-Based Dual-Branch Network for Image Demoireing](https://mlsanity.com/paper/2007.07173/) (2020)
- [SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains](https://mlsanity.com/paper/2012.05535/) (2020)
- [Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks](https://mlsanity.com/paper/1809.06647/) (2018)
- [Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video](https://mlsanity.com/paper/2008.00499/) (2020)
- [Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution](https://mlsanity.com/paper/1809.04508/) (2018)
- [MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks](https://mlsanity.com/paper/1903.06048/) (2019)
- [Image Super-Resolution Using a Wavelet-based Generative Adversarial Network](https://mlsanity.com/paper/1907.10213/) (2019)

## Built on by (most cited first, 17 of 17)

- [VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder](https://mlsanity.com/paper/2205.06803/) (2022)
- [HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field](https://mlsanity.com/paper/2309.17128/) (2023)
- [Neural Fourier Filter Bank](https://mlsanity.com/paper/2212.01735/) (2022)
- [Wavelet-Packets for Deepfake Image Analysis and Detection](https://mlsanity.com/paper/2106.09369/) (2021)
- [Rewriting Geometric Rules of a GAN](https://mlsanity.com/paper/2207.14288/) (2022)
- [GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting](https://mlsanity.com/paper/2205.07162/) (2022)
- [DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data](https://mlsanity.com/paper/2306.14153/) (2023)
- [USIS: Unsupervised Semantic Image Synthesis](https://mlsanity.com/paper/2109.14715/) (2021)
- [WINE: Wavelet-Guided GAN Inversion and Editing for High-Fidelity Refinement](https://mlsanity.com/paper/2210.09655/) (2022)
- [Towards Pragmatic Semantic Image Synthesis for Urban Scenes](https://mlsanity.com/paper/2305.09726/) (2023)
- [Wavelet-based Unsupervised Label-to-Image Translation](https://mlsanity.com/paper/2305.09647/) (2023)
- [Conditionally Strongly Log-Concave Generative Models](https://mlsanity.com/paper/2306.00181/) (2023)
- [Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative Models](https://mlsanity.com/paper/2208.02089/) (2022)
- [Multi-Channel Cross Modal Detection of Synthetic Face Images](https://mlsanity.com/paper/2311.16773/) (2023)
- [Hybrid Generative Models for Two-Dimensional Datasets](https://mlsanity.com/paper/2106.00203/) (2021)
- [Explicit Use of Fourier Spectrum in Generative Adversarial Networks](https://mlsanity.com/paper/2208.01265/) (2022)
- [Frequency-Aware Dual-Stream Learning for Balanced Realism and Fidelity in Electron Microscopy Imaging](https://mlsanity.com/paper/2607.22765/) (2026)

## Abstract (arXiv)

In recent years, considerable progress has been made in the visual quality of Generative Adversarial Networks (GANs). Even so, these networks still suffer from degradation in quality for high-frequency content, stemming from a spectrally biased architecture, and similarly unfavorable loss functions. To address this issue, we present a novel general-purpose Style and WAvelet based GAN (SWAGAN) that implements progressive generation in the frequency domain. SWAGAN incorporates wavelets throughout its generator and discriminator architectures, enforcing a frequency-aware latent representation at every step of the way. This approach yields enhancements in the visual quality of the generated images, and considerably increases computational performance. We demonstrate the advantage of our method by integrating it into the SyleGAN2 framework, and verifying that content generation in the wavelet domain leads to higher quality images with more realistic high-frequency content. Furthermore, we verify that our model's latent space retains the qualities that allow StyleGAN to serve as a basis for a multitude of editing tasks, and show that our frequency-aware approach also induces improved downstream visual quality.