A comprehensive survey on data-efficient GANs in image generation
Generative Adversarial Networks (GANs) have achieved remarkable achievements in image
synthesis. These successes of GANs rely on large scale datasets, requiring too much cost …
synthesis. These successes of GANs rely on large scale datasets, requiring too much cost …
Complexity matters: Rethinking the latent space for generative modeling
In generative modeling, numerous successful approaches leverage a low-dimensional
latent space, eg, Stable Diffusion models the latent space induced by an encoder and …
latent space, eg, Stable Diffusion models the latent space induced by an encoder and …
Variational Wasserstein gradient flow
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 …
problems over the space of probability distributions. A recent trend is to use the well-known …
Generative modeling through the semi-dual formulation of unbalanced optimal transport
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 …
while minimizing a given cost function. In this regard, OT between tractable prior distribution …
Wordgesture-GAN: modeling word-gesture movement with generative adversarial network
Word-gesture production models that can synthesize word-gestures are critical to the
training and evaluation of word-gesture keyboard decoders. We propose WordGesture …
training and evaluation of word-gesture keyboard decoders. We propose WordGesture …
Detail me more: Improving gan's photo-realism of complex scenes
Generative models can synthesize photo-realistic images of a single object. For example, for
human faces, algorithms learn to model the local shape and shading of the face …
human faces, algorithms learn to model the local shape and shading of the face …
[HTML][HTML] Autoencoder-based conditional optimal transport generative adversarial network for medical image generation
Medical image generation has recently garnered significant interest among researchers.
However, the primary generative models, such as Generative Adversarial Networks (GANs) …
However, the primary generative models, such as Generative Adversarial Networks (GANs) …
Learning deep latent variable models by short-run mcmc inference with optimal transport correction
Learning latent variable models with deep top-down architectures typically requires inferring
the latent variables for each training example based on the posterior distribution of these …
the latent variables for each training example based on the posterior distribution of these …
Analyzing and Improving OT-based Adversarial Networks
Optimal Transport (OT) problem aims to find a transport plan that bridges two distributions
while minimizing a given cost function. OT theory has been widely utilized in generative …
while minimizing a given cost function. OT theory has been widely utilized in generative …
A mathematical framework for learning probability distributions
H Yang - arXiv preprint arXiv:2212.11481, 2022 - arxiv.org
The modeling of probability distributions, specifically generative modeling and density
estimation, has become an immensely popular subject in recent years by virtue of its …
estimation, has become an immensely popular subject in recent years by virtue of its …