# Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer

arXiv: https://arxiv.org/abs/1904.02296 · HTML: https://mlsanity.com/paper/1904.02296/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

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

- [SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction](https://mlsanity.com/paper/2310.20700/) (2023)
- [DG-Font: Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2104.03064/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [E2Style: Improve the Efficiency and Effectiveness of StyleGAN Inversion](https://mlsanity.com/paper/2104.07661/) (2021)
- [CF-Font: Content Fusion for Few-shot Font Generation](https://mlsanity.com/paper/2303.14017/) (2023)
- [CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models](https://mlsanity.com/paper/2404.15677/) (2024)
- [TVG: A Training-free Transition Video Generation Method with Diffusion Models](https://mlsanity.com/paper/2408.13413/) (2024)
- [Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity](https://mlsanity.com/paper/2102.11382/) (2021)
- [Style Transfer with Diffusion Models for Synthetic-to-Real Domain Adaptation](https://mlsanity.com/paper/2505.16360/) (2025)
- [WISE: Whitebox Image Stylization by Example-based Learning](https://mlsanity.com/paper/2207.14606/) (2022)
- [Decoupled Residual Denoising Diffusion Models for Unified and Data Efficient Image-to-Image Translation](https://mlsanity.com/paper/2606.01048/) (2026)
- [PeaceGAN: A GAN-based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier](https://mlsanity.com/paper/2103.15469/) (2021)
- [DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2212.14742/) (2022)
- [Controllable Multi-domain Semantic Artwork Synthesis](https://mlsanity.com/paper/2308.10111/) (2023)
- [Enhancing Scene Transition Awareness in Video Generation via Post-Training](https://mlsanity.com/paper/2507.18046/) (2025)
- [Style Generation in Robot Calligraphy with Deep Generative Adversarial Networks](https://mlsanity.com/paper/2312.09673/) (2023)
- [Neural-Polyptych: Content Controllable Painting Recreation for Diverse Genres](https://mlsanity.com/paper/2409.19690/) (2024)

## Abstract (arXiv)

Style transfer describes the rendering of an image semantic content as different artistic styles. Recently, generative adversarial networks (GANs) have emerged as an effective approach in style transfer by adversarially training the generator to synthesize convincing counterfeits. However, traditional GAN suffers from the mode collapse issue, resulting in unstable training and making style transfer quality difficult to guarantee. In addition, the GAN generator is only compatible with one style, so a series of GANs must be trained to provide users with choices to transfer more than one kind of style. In this paper, we focus on tackling these challenges and limitations to improve style transfer. We propose adversarial gated networks (Gated GAN) to transfer multiple styles in a single model. The generative networks have three modules: an encoder, a gated transformer, and a decoder. Different styles can be achieved by passing input images through different branches of the gated transformer. To stabilize training, the encoder and decoder are combined as an autoencoder to reconstruct the input images. The discriminative networks are used to distinguish whether the input image is a stylized or genuine image. An auxiliary classifier is used to recognize the style categories of transferred images, thereby helping the generative networks generate images in multiple styles. In addition, Gated GAN makes it possible to explore a new style by investigating styles learned from artists or genres. Our extensive experiments demonstrate the stability and effectiveness of the proposed model for multistyle transfer.