Unsupervised image-to-image translation is the task of doing image-to-image translation without ground truth image-to-image pairings.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Unsupervised image-to-image translation aims to learn a mapping between several visual domains by using unpaired training pairs.
We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner.
The goal of unsupervised image-to-image translation is to map images from one domain to another without the ground truth correspondence between the two domains.
Existing approaches have been proposed to tackle unsupervised image-to-image translation in recent years.
Deep learning approaches have become the standard solution to many problems in computer vision and robotics, but obtaining proper and sufficient training data is often a problem, as human labor is often error prone, time consuming and expensive.
Unsupervised image-to-image translation is the task of translating an image from one domain to another in the absence of any paired training examples and tends to be more applicable to practical applications.
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.
Extensive experiments demonstrate the superior performance of our method to other state-of-the-art approaches, especially in the challenging near-rigid and non-rigid objects translation tasks.
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).