# ViTGAN: Training GANs with Vision Transformers

**Authors:** Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, Ce Liu  
**arXiv:** [2107.04589](https://arxiv.org/abs/2107.04589)  
**Date:** 2021-07-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2107.04589v1/

## Key points

- Vision Transformers (ViTs) have demonstrated impressive performance in image recognition tasks with fewer vision-specific biases compared to traditional Convolutional Neural Networks (CNNs).
- Kwonjoon Lee and colleagues investigate whether ViTs can also be used for image generation tasks by integrating the ViT architecture into Generative Adversarial Networks (GANs).
- Existing regularization methods for GANs do not interact well with self-attention mechanisms in ViTs, leading to instability issues during training.
- The authors introduce novel regularization techniques specifically designed for training GANs with ViTs named ViTGAN.
- ViTGAN achieves comparable performance to state-of-the-art CNN-based StyleGAN2 on three popular datasets: CIFAR-10, CelebA, and LSUN bedroom.
- Empirical results demonstrate that their method produces high-quality images with sharp details and diverse styles.
- Combining Vision Transformers and Generative Adversarial Networks could be a promising direction for future research in image generation tasks.

## Summary

In recent years, Vision Transformers (ViTs) have demonstrated impressive performance in image recognition tasks while requiring fewer vision-specific inductive biases compared to traditional Convolutional Neural Networks (CNNs). In this paper titled "ViTGAN: Training GANs with Vision Transformers," Kwonjoon Lee and colleagues investigate whether ViTs can also be used for image generation tasks. To achieve this, they integrate the ViT architecture into Generative Adversarial Networks (GANs). However, during training, they observe that existing regularization methods for GANs do not interact well with self-attention mechanisms in ViTs, leading to instability issues. To address this problem, the authors introduce novel regularization techniques specifically designed for training GANs with ViTs. The proposed approach named ViTGAN achieves comparable performance to state-of-the-art CNN-based StyleGAN2 on three popular datasets: CIFAR-10, CelebA, and LSUN bedroom. The authors' empirical results demonstrate that their method produces high-quality images with sharp details and diverse styles. Furthermore, these findings suggest that integrating ViTs into GANs could be a promising direction for future research in image generation tasks. Overall, the findings of this paper indicate that ViTGAN is a viable approach for image generation tasks and can produce high quality images with sharp details and diverse styles. This suggests that combining Vision Transformers and Generative Adversarial Networks could be a promising direction for future research in image generation tasks.