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Found 5 papers, 4 papers with code
Real-time Localized Photorealistic Video Style Transfer
We present a novel algorithm for transferring artistic styles of semantically meaningful local regions of an image onto local regions of a target video while preserving its photorealism.
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera.
Learning to Approximate a Bregman Divergence
Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning.
Moment Matching for Multi-Source Domain Adaptation
Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
W-Net: A Deep Model for Fully Unsupervised Image Segmentation
While significant attention has been recently focused on designing supervised deep semantic segmentation algorithms for vision tasks, there are many domains in which sufficient supervised pixel-level labels are difficult to obtain.
