Image-to-Image Translation
491 papers with code • 37 benchmarks • 29 datasets
Image-to-Image Translation is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.
( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )
Benchmarks
These leaderboards are used to track progress in Image-to-Image Translation
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Libraries
Use these libraries to find Image-to-Image Translation models and implementationsDatasets
Cityscapes
KITTI
ADE20K
CelebA-HQ
SYNTHIA
GTA5
DeepFashion
Perceptual Similarity
AFHQ
COCO-Stuff
Subtasks
Latest papers with no code
GAN with Skip Patch Discriminator for Biological Electron Microscopy Image Generation
Generating realistic electron microscopy (EM) images has been a challenging problem due to their complex global and local structures.
StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation
Most image-to-image translation models postulate that a unique correspondence exists between the semantic classes of the source and target domains.
Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer
Finally, a detailed qualitative assessment by five medical doctors indicated a lack of low level feature discovery in image to image translation tasks.
Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis
With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field.
High-Resolution Image Translation Model Based on Grayscale Redefinition
Image-to-image translation is a technique that focuses on transferring images from one domain to another while maintaining the essential content representations.
DreamFlow: High-Quality Text-to-3D Generation by Approximating Probability Flow
Recent progress in text-to-3D generation has been achieved through the utilization of score distillation methods: they make use of the pre-trained text-to-image (T2I) diffusion models by distilling via the diffusion model training objective.
Could We Generate Cytology Images from Histopathology Images? An Empirical Study
Automation in medical imaging is quite challenging due to the unavailability of annotated datasets and the scarcity of domain experts.
Versatile Defense Against Adversarial Attacks on Image Recognition
When facing the PGD attack and the MI-FGSM attack, versatile defense model even outperforms the attack-specific models trained based on these two attacks.
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives
This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challenge in 2023.
Scene Depth Estimation from Traditional Oriental Landscape Paintings
To address the problem of scene depth estimation from oriental landscape painting images, we propose a novel framework that consists of two-step Image-to-Image translation method with CLIP-based image matching at the front end to predict the real scene image that best matches with the given oriental landscape painting image.
