Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging
visual learning tasks, which can be cast as a feature distribution matching problem. With the …

Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a
set of normal training samples to identify abnormal samples in test data. Most existing AD …

Recently, deep learning approaches using CycleGAN have been demonstrated as a
powerful unsupervised learning scheme for low-dose CT denoising. Unfortunately, one of …

Style transfer aims to render the content of a given image in the graphical/artistic style of
another image. The fundamental concept underlying Neural Style Transfer (NST) is to …

We propose an image restoration algorithm that can control the perceptual quality and/or the
mean square error (MSE) of any pre-trained model, trading one over the other at test time …

Wasserstein barycenters provide a geometric notion of the weighted average of probability
measures based on optimal transport. In this paper, we present a scalable algorithm to …

Wasserstein barycenters have become popular due to their ability to represent the average
of probability measures in a geometrically meaningful way. In this paper, we present an …

Multi-contrast MRI images use different echo and repetition times to highlight different
tissues. However, not all desired image contrasts may be available due to scan-time …

Many traditional computer vision algorithms generate realistic images by requiring that each
patch in the generated image be similar to a patch in a training image and vice versa …

X-ray computed tomography (CT) uses different filter kernels to highlight different structures.
Since the raw sinogram data is usually removed after the reconstruction, in case there is …