Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …

In generative modeling, numerous successful approaches leverage a low-dimensional
latent space, eg, Stable Diffusion models the latent space induced by an encoder and …

Wasserstein gradient flow has emerged as a promising approach to solve optimization
problems over the space of probability distributions. A recent trend is to use the well-known …

Optimal Transport (OT) problem investigates a transport map that bridges two distributions
while minimizing a given cost function. In this regard, OT between tractable prior distribution …

Passive non-line-of-sight (NLOS) imaging has drawn great attention in recent years.
However, all existing methods are in common limited to simple hidden scenes, low-quality …

Sampling from diffusion probabilistic models (DPMs) can be viewed as a piecewise
distribution transformation, which generally requires hundreds or thousands of steps of the …

Deep models have achieved impressive success in class-imbalanced visual recognition. In
the view of optimal transport, the current evaluation protocol for class-imbalanced visual …

Abstract Generative Adversarial Networks (GANs) have shown compelling results in various
tasks and applications in recent years. However, mode collapse remains a critical problem …

Recent studies have shown that the performance of forgery detection can be improved with
diverse and challenging Deepfakes datasets. However, due to the lack of Deepfakes …

We consider Generative Adversarial Networks (GANs) and address the underlying
functional optimization problem ab initio within a variational setting. Strictly speaking, the …