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Found 11 papers, 5 papers with code
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model
Instead, we propose to use noise contrastive estimation (NCE) to discriminatively learn the EBM through density ratio estimation between the latent prior density and latent posterior density.
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
To the best of our knowledge, denoising diffusion GAN is the first model that reduces sampling cost in diffusion models to an extent that allows them to be applied to real-world applications inexpensively.
Ranked #8 on
Image Generation
on CelebA-HQ 256x256
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 outlier or not using only the information from unlabelled inlier data, is an important but challenging task.
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss
Doing so allows us to study the density induced by the dynamics (if the dynamics are invertible), and connect with GANs by treating the dynamics as generator models, the initial values as latent variables and the loss as optimizing a critic defined by the very same energy that determines the generator through its gradient.
ControlVAE: Tuning, Analytical Properties, and Performance Analysis
ControlVAE is a new variational autoencoder (VAE) framework that combines the automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value.
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
VAEBM captures the overall mode structure of the data distribution using a state-of-the-art VAE and it relies on its EBM component to explicitly exclude non-data-like regions from the model and refine the image samples.
Ranked #1 on
Image Generation
on Stacked MNIST
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 quality of pre-trained likelihood based generative models.
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
An important application of generative modeling should be the ability to detect out-of-distribution (OOD) samples by setting a threshold on the likelihood.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A Method to Model Conditional Distributions with Normalizing Flows
In particular, we use our proposed method to analyze inverse problems with invertible neural networks by maximizing the posterior likelihood.
Understanding Limitation of Two Symmetrized Orders by Worst-case Complexity
Recently, Coordinate Descent (CD) with cyclic order was shown to be $O(n^2)$ times slower than randomized versions in the worst-case.
Generative Latent Flow
In this work, we propose the Generative Latent Flow (GLF), an algorithm for generative modeling of the data distribution.
Ranked #1 on
Image Generation
on Fashion-MNIST
