Multimodal Unsupervised Image-To-Image Translation
14 papers with code • 6 benchmarks • 4 datasets
Multimodal unsupervised image-to-image translation is the task of producing multiple translations to one domain from a single image in another domain.
( Image credit: MUNIT: Multimodal UNsupervised Image-to-image Translation )
Benchmarks
These leaderboards are used to track progress in Multimodal Unsupervised Image-To-Image Translation
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Libraries
Use these libraries to find Multimodal Unsupervised Image-To-Image Translation models and implementations
CelebA-HQ
AFHQ
CATS
FFHQ-Aging
Latest papers with no code
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e. g., random noise samples to diverse pathological images) or image-to-image GANs (e. g., a benign image to a malignant one).
Semi-Supervised Image-to-Image Translation
The advantage of using such an approach is that the image-to-image translation is semi-supervised, independant of image segmentation and inherits the properties of generative adversarial networks tending to produce realistic.
Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation
In multimodal unsupervised image-to-image translation tasks, the goal is to translate an image from the source domain to many images in the target domain.
