# UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation

arXiv: https://arxiv.org/abs/2203.02557 · HTML: https://mlsanity.com/paper/2203.02557/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 84 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 30 of 66)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [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)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)

## Built on by (most cited first, 30 of 42)

- [CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation](https://mlsanity.com/paper/2310.13165/) (2023)
- [Generative Artificial Intelligence: A Systematic Review and Applications](https://mlsanity.com/paper/2405.11029/) (2024)
- [UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis](https://mlsanity.com/paper/2510.15710/) (2025)
- [Synthetic Latent Fingerprint Generator](https://mlsanity.com/paper/2208.13811/) (2022)
- [Spectral Normalization and Dual Contrastive Regularization for Image-to-Image Translation](https://mlsanity.com/paper/2304.11319/) (2023)
- [StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining](https://mlsanity.com/paper/2403.11340/) (2024)
- [Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs](https://mlsanity.com/paper/2404.10700/) (2024)
- [Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2302.08641/) (2023)
- [UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2303.16280/) (2023)
- [Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis](https://mlsanity.com/paper/2406.18967/) (2024)
- [Exploring Generative AI for Sim2Real in Driving Data Synthesis](https://mlsanity.com/paper/2404.09111/) (2024)
- [Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers](https://mlsanity.com/paper/2404.09326/) (2024)
- [F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation](https://mlsanity.com/paper/2404.12650/) (2024)
- [The SkipSponge Attack: Sponge Weight Poisoning of Deep Neural Networks](https://mlsanity.com/paper/2402.06357/) (2024)
- [D4D: An RGBD diffusion model to boost monocular depth estimation](https://mlsanity.com/paper/2403.07516/) (2024)
- [PosDiffAE: Position-aware Diffusion Auto-encoder For High-Resolution Brain Tissue Classification Incorporating Artifact Restoration](https://mlsanity.com/paper/2507.02405/) (2025)
- [Unsupervised Decomposition and Recombination with Discriminator-Driven Diffusion Models](https://mlsanity.com/paper/2601.22057/) (2026)
- [RADIO: Reference-Agnostic Dubbing Video Synthesis](https://mlsanity.com/paper/2309.01950/) (2023)
- [Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2309.13188/) (2023)
- [Reliable Multi-modal Medical Image-to-image Translation Independent of Pixel-wise Aligned Data](https://mlsanity.com/paper/2408.14270/) (2024)
- [I2I-Galip: Unsupervised Medical Image Translation Using Generative Adversarial CLIP](https://mlsanity.com/paper/2409.12399/) (2024)
- [Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image Translation](https://mlsanity.com/paper/2409.16706/) (2024)
- [CSHNet: A Novel Information Asymmetric Image Translation Method](https://mlsanity.com/paper/2501.10197/) (2025)
- [Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine](https://mlsanity.com/paper/2503.19248/) (2025)
- [Unpaired Translation of Chest X-ray Images for Lung Opacity Diagnosis via Adaptive Activation Masks and Cross-Domain Alignment](https://mlsanity.com/paper/2503.19860/) (2025)
- [Learning Deblurring Texture Prior from Unpaired Data with Diffusion Model](https://mlsanity.com/paper/2507.13599/) (2025)
- [Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses](https://mlsanity.com/paper/2304.12858/) (2023)
- [A Vessel-Segmentation-Based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis](https://mlsanity.com/paper/2306.02901/) (2023)
- [Generative Adversarial Shaders for Real-Time Realism Enhancement](https://mlsanity.com/paper/2306.04629/) (2023)
- [LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images](https://mlsanity.com/paper/2503.16376/) (2025)

## Abstract (arXiv)

Unpaired image-to-image translation has broad applications in art, design, and scientific simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings between two unpaired image domains via generative-adversarial networks (GAN) coupled with the cycle-consistency constraint, while more recent works promote one-to-many mapping to boost diversity of the translated images. Motivated by scientific simulation and one-to-one needs, this work revisits the classic CycleGAN framework and boosts its performance to outperform more contemporary models without relaxing the cycle-consistency constraint. To achieve this, we equip the generator with a Vision Transformer (ViT) and employ necessary training and regularization techniques. Compared to previous best-performing models, our model performs better and retains a strong correlation between the original and translated image. An accompanying ablation study shows that both the gradient penalty and self-supervised pre-training are crucial to the improvement. To promote reproducibility and open science, the source code, hyperparameter configurations, and pre-trained model are available at https://github.com/LS4GAN/uvcgan.