Sdedit: Guided image synthesis and editing with stochastic differential equations
Guided image synthesis enables everyday users to create and edit photo-realistic images
with minimum effort. The key challenge is balancing faithfulness to the user input (eg, hand …
with minimum effort. The key challenge is balancing faithfulness to the user input (eg, hand …
How to train your energy-based models
Energy-Based Models (EBMs), also known as non-normalized probabilistic models, specify
probability density or mass functions up to an unknown normalizing constant. Unlike most …
probability density or mass functions up to an unknown normalizing constant. Unlike most …
Implicit generation and modeling with energy based models
Y Du, I Mordatch - Advances in Neural Information …, 2019 - proceedings.neurips.cc
Energy based models (EBMs) are appealing due to their generality and simplicity in
likelihood modeling, but have been traditionally difficult to train. We present techniques to …
likelihood modeling, but have been traditionally difficult to train. We present techniques to …
Learning generative vision transformer with energy-based latent space for saliency prediction
Vision transformer networks have shown superiority in many computer vision tasks. In this
paper, we take a step further by proposing a novel generative vision transformer with latent …
paper, we take a step further by proposing a novel generative vision transformer with latent …
Learning non-convergent non-persistent short-run mcmc toward energy-based model
This paper studies a curious phenomenon in learning energy-based model (EBM) using
MCMC. In each learning iteration, we generate synthesized examples by running a non …
MCMC. In each learning iteration, we generate synthesized examples by running a non …
Improved contrastive divergence training of energy based models
Contrastive divergence is a popular method of training energy-based models, but is known
to have difficulties with training stability. We propose an adaptation to improve contrastive …
to have difficulties with training stability. We propose an adaptation to improve contrastive …
Learning latent space energy-based prior model
We propose an energy-based model (EBM) in the latent space of a generator model, so that
the EBM serves as a prior model that stands on the top-down network of the generator …
the EBM serves as a prior model that stands on the top-down network of the generator …
On the anatomy of mcmc-based maximum likelihood learning of energy-based models
This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in
unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of …
unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of …
Generalized energy based models
We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These
models combine two trained components: a base distribution (generally an implicit model) …
models combine two trained components: a base distribution (generally an implicit model) …