Data to 7 Oct 2026

DRIT++: Diverse Image-to-Image Translation via Disentangled Representations

arXiv 1905.01270 ↗First posted 2 May 2019cs.CVcited by 244

Not on the map. Why?

Abstract (arXiv)

Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for this task: 1) lack of aligned training pairs and 2) multiple possible outputs from a single input image. In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images. To synthesize diverse outputs, we propose to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain-specific attribute space. Our model takes the encoded content features extracted from a given input and attribute vectors sampled from the attribute space to synthesize diverse outputs at test time. To handle unpaired training data, we introduce a cross-cycle consistency loss based on disentangled representations. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks without paired training data. For quantitative evaluations, we measure realism with user study and Fr'echet inception distance, and measure diversity with the perceptual distance metric, Jensen-Shannon divergence, and number of statistically-different bins.

Key facts. Cited by 244 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 106; 2022: 57; 2023: 40; 2024: 22; 2025: 13; 2026: 5.

244 papers.

Sort:

By area:

2023One-step Diffusion with Distribution Matching Distillationcited by 978
2022BBDM: Image-to-image Translation with Brownian Bridge Diffusion Modelscited by 222
2021GAN Prior Embedded Network for Blind Face Restoration in the Wildcited by 197
2022NeRF-Art: Text-Driven Neural Radiance Fields Stylizationcited by 126
2021Image-to-Image Translation: Methods and Applicationscited by 116
2021Regularizing Generative Adversarial Networks under Limited Datacited by 105
2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 95
2021Only a Matter of Style: Age Transformation Using a Style-Based Regression Modelcited by 92
2021Learning by Watching: Physical Imitation of Manipulation Skills from Human Videoscited by 87
2023ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Modelscited by 75
2021The Spatially-Correlative Loss for Various Image Translation Taskscited by 66
2021Dual Contrastive Learning for Unsupervised Image-to-Image Translationcited by 65

38 more

2022Motion Puzzle: Arbitrary Motion Style Transfer by Body Partcited by 64
2021Image-to-image Translation via Hierarchical Style Disentanglementcited by 63
2021InfinityGAN: Towards Infinite-Pixel Image Synthesiscited by 61
2022BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pixcited by 61
2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
2021Anycost GANs for Interactive Image Synthesis and Editingcited by 54
2021Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigationcited by 53
2021Learning the Predictability of the Futurecited by 48
2021StyleAlign: Analysis and Applications of Aligned StyleGAN Modelscited by 48
2021BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generationcited by 47
2024XoFTR: Cross-modal Feature Matching Transformercited by 45
2022Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Taskscited by 42
2021Unbalanced Feature Transport for Exemplar-based Image Translationcited by 41
2021Fighting deepfakes by detecting GAN DCT anomaliescited by 39
2022QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translationcited by 39
2021DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Networkcited by 37
2021Sketch Your Own GANcited by 37
2023Exploiting Diffusion Prior for Generalizable Dense Predictioncited by 37
2021Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translationcited by 36
2021Bridging the Gap between Events and Frames through Unsupervised Domain Adaptationcited by 36
2021Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translationcited by 35
2021Video Autoencoder: self-supervised disentanglement of static 3D structure and motioncited by 34
2022Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshescited by 34
2021Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representationscited by 31
2023Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labelscited by 30
2022InstaFormer: Instance-Aware Image-to-Image Translation with Transformercited by 28
2021CoMoGAN: continuous model-guided image-to-image translationcited by 27
2021Where and What? Examining Interpretable Disentangled Representationscited by 27
2021AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generationcited by 25
2021UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimationcited by 25
2024CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Modelscited by 24
2021Representation Disentanglement for Multi-modal brain MR Analysiscited by 22
2023Controllable Inversion of Black-Box Face Recognition Models via Diffusioncited by 22
2021Long-Term Temporally Consistent Unpaired Video Translation from Simulated Surgical 3D Datacited by 21
2021Semi-supervised Synthesis of High-Resolution Editable Textures for 3D Humanscited by 21
2021Exemplar-Based 3D Portrait Stylizationcited by 21
2021Discovering Distinctive "Semantics" in Super-Resolution Networkscited by 20
2022Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Drivingcited by 20

50 most cited of 244 All 244 (JSON)