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09/19/2022
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model
This paper studies the fundamental problem of learning energy-based mode...
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12/15/2021
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
A wide variety of deep generative models has been developed in the past ...
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05/19/2021
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?
Unsupervised outlier detection, which predicts if a test sample is an ou...
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02/23/2021
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss
Maximum likelihood estimation is widely used in training Energy-based mo...
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10/31/2020
ControlVAE: Tuning, Analytical Properties, and Performance Analysis
This paper reviews the novel concept of controllable variational autoenc...
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10/01/2020
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
Energy-based models (EBMs) have recently been successful in representing...
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06/15/2020
Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy
In this paper, we present a general method that can improve the sample q...
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03/06/2020
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
Deep probabilistic generative models enable modeling the likelihoods of ...
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11/05/2019
A Method to Model Conditional Distributions with Normalizing Flows
In this work, we investigate the use of normalizing flows to model condi...
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10/10/2019
Understanding Limitation of Two Symmetrized Orders by Worst-case Complexity
It was recently found that the standard version of multi-block cyclic AD...
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05/24/2019
