Personalized years, influencing influencing matching personalized userpreferenceideal [1].

Serge Belongie, member of the Computer Science department and Associate Dean at Cornell Tech, has been named Andrew H. and Ann R. Tisch Professor. In response to his investiture as an endowed chair, which began on April 1st, Belongie says "I wish I had the words to express my gratitude for this remarkable honor." In his capacity as Associate Dean at Cornell Tech, Belongie is "busy with coronavirus pandemic-related planning for Fall semester course offerings." As professor, he is working on "growing our cross-campus research efforts in Mixed Reality." The latter initiative "gathers efforts from across Cornell's campuses that relate to augmented and virtual reality, and their core disciplines of computer vision, computer graphics, and human-computer interaction."

Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any pairs of corresponding images. While this conditional distribution is inherently multimodal, existing approaches make an overly simplified assumption, modeling it as a deterministic one-to-one mapping. As a result, they fail to generate diverse outputs from a given source domain image. To address this limitation, we propose a Multimodal Unsupervised Image-to-image Translation (MUNIT) framework. We assume that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties. To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain. We analyze the proposed framework and establish several theoretical results. Extensive experiments with comparisons to the state-of-the-art approaches further demonstrates the advantage of the proposed framework. Moreover, our framework allows users to control the style of translation outputs by providing an example style image. Code and pretrained models are available at https://github.com/nvlabs/MUNIT.