# Weather GAN: Multi-Domain Weather Translation Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2103.05422 · HTML: https://mlsanity.com/paper/2103.05422/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 53 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [Attentive Generative Adversarial Network for Raindrop Removal from a Single Image](https://mlsanity.com/paper/1711.10098/) (2017)
- [Image to Image Translation for Domain Adaptation](https://mlsanity.com/paper/1712.00479/) (2017)
- [Deep Photo Style Transfer](https://mlsanity.com/paper/1703.07511/) (2017)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing](https://mlsanity.com/paper/1805.05308/) (2018)
- [Unsupervised Attention-guided Image to Image Translation](https://mlsanity.com/paper/1806.02311/) (2018)
- [InstaGAN: Instance-aware Image-to-Image Translation](https://mlsanity.com/paper/1812.10889/) (2018)
- [Attention-GAN for Object Transfiguration in Wild Images](https://mlsanity.com/paper/1803.06798/) (2018)
- [High-Resolution Daytime Translation Without Domain Labels](https://mlsanity.com/paper/2003.08791/) (2020)
- [DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)](https://mlsanity.com/paper/1802.06454/) (2018)
- [End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks](https://mlsanity.com/paper/2011.05552/) (2020)
- [A CNN-RNN Architecture for Multi-Label Weather Recognition](https://mlsanity.com/paper/1904.10709/) (2019)
- [Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention](https://mlsanity.com/paper/1806.06195/) (2018)
- [Castle in the Sky: Dynamic Sky Replacement and Harmonization in Videos](https://mlsanity.com/paper/2010.11800/) (2020)

## Built on by (most cited first, 18 of 18)

- [Underwater Light Field Retention : Neural Rendering for Underwater Imaging](https://mlsanity.com/paper/2203.11006/) (2022)
- [Distracting Downpour: Adversarial Weather Attacks for Motion Estimation](https://mlsanity.com/paper/2305.06716/) (2023)
- [ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods](https://mlsanity.com/paper/2110.02871/) (2021)
- [AGI for Agriculture](https://mlsanity.com/paper/2304.06136/) (2023)
- [SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous Driving](https://mlsanity.com/paper/2408.01430/) (2024)
- [Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time](https://mlsanity.com/paper/2505.19919/) (2025)
- [StyleFlow For Content-Fixed Image to Image Translation](https://mlsanity.com/paper/2207.01909/) (2022)
- [A-BDD: Leveraging Data Augmentations for Safe Autonomous Driving in Adverse Weather and Lighting](https://mlsanity.com/paper/2408.06071/) (2024)
- [Unsupervised Neural Rendering for Image Hazing](https://mlsanity.com/paper/2107.06681/) (2021)
- [Hierarchy Flow For High-Fidelity Image-to-Image Translation](https://mlsanity.com/paper/2308.06909/) (2023)
- [ClimateGS: Real-Time Climate Simulation with 3D Gaussian Style Transfer](https://mlsanity.com/paper/2503.14845/) (2025)
- [FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model](https://mlsanity.com/paper/2606.20110/) (2026)
- [Semantic-Aware, Physics-Informed, Geometry-Grounded Weather Video Synthesis](https://mlsanity.com/paper/2606.29020/) (2026)
- [A comparative study of stochastic and deep generative models for multisite precipitation synthesis](https://mlsanity.com/paper/2107.08074/) (2021)
- [Vision-Language Modeling with Regularized Spatial Transformer Networks for All Weather Crosswind Landing of Aircraft](https://mlsanity.com/paper/2405.05574/) (2024)
- [Intensity and Texture Correction of Omnidirectional Image Using Camera Images for Indirect Augmented Reality](https://mlsanity.com/paper/2405.16008/) (2024)
- [Enhancing Robustness of Human Detection Algorithms in Maritime SAR through Augmented Aerial Images to Simulate Weather Conditions](https://mlsanity.com/paper/2408.13766/) (2024)
- [A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline](https://mlsanity.com/paper/2605.12608/) (2026)

## Abstract (arXiv)

In this paper, a new task is proposed, namely, weather translation, which refers to transferring weather conditions of the image from one category to another. It is important for photographic style transfer. Although lots of approaches have been proposed in traditional image translation tasks, few of them can handle the multi-category weather translation task, since weather conditions have rich categories and highly complex semantic structures. To address this problem, we develop a multi-domain weather translation approach based on generative adversarial networks (GAN), denoted as Weather GAN, which can achieve the transferring of weather conditions among sunny, cloudy, foggy, rainy and snowy. Specifically, the weather conditions in the image are determined by various weather-cues, such as cloud, blue sky, wet ground, etc. Therefore, it is essential for weather translation to focus the main attention on weather-cues. To this end, the generator of Weather GAN is composed of an initial translation module, an attention module and a weather-cue segmentation module. The initial translation module performs global translation during generation procedure. The weather-cue segmentation module identifies the structure and exact distribution of weather-cues. The attention module learns to focus on the interesting areas of the image while keeping other areas unaltered. The final generated result is synthesized by these three parts. This approach suppresses the distortion and deformation caused by weather translation. our approach outperforms the state-of-the-arts has been shown by a large number of experiments and evaluations.