Image-to-image translation (I2I) aims at transferring the content representation from an input
domain to an output one, bouncing along different target domains. Recent I2I generative …

Image-to-image translation has recently achieved remarkable results. But despite current
success, it suffers from inferior performance when translations between classes require …

The main challenges of image-to-image (I2I) translation are to make the translated image
realistic and retain as much information from the source domain as possible. To address this …

Abstract Image-to-image (I2I) translation has matured in recent years and is able to generate
high-quality realistic images. However, despite current success, it still faces important …

Despite significant advances in image-to-image (I2I) translation with generative adversarial
networks (GANs), it remains challenging to effectively translate an image to a set of diverse …

Over the past few years, image-to-image (I2I) translation methods have been proposed to
translate a given image into diverse outputs. Despite the impressive results, they mainly …

Interest in image-to-image translation has grown substantially in recent years with the
success of unsupervised models based on the cycle-consistency assumption. The …

Image translation is a burgeoning field in computer vision where the goal is to learn the
mapping between an input image and an output image. However, most recent methods …

Image-to-image (I2I) translation has become a key asset for generative adversarial
networks. Convolutional neural networks (CNNs), despite having a significant performance …

We introduce a new architecture called a conditional invertible neural network (cINN), and
use it to address the task of diverse image-to-image translation for natural images. This is …