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Found 8 papers, 4 papers with code
A Residual Diffusion Model for High Perceptual Quality Codec Augmentation
Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data.
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss
In this study, we propose a novel loss objective combined with COT-GAN based on an autoregressive embedding to reinforce the learning of spatio-temporal dynamics.
Conditional COT-GAN for Video Prediction with Kernel Smoothing
The resulting kernel conditional COT-GAN algorithm is illustrated with an application for video prediction.
Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Deep generative models are increasingly used to gain insights in the geospatial data domain, e. g., for climate data.
COT-GAN: Generating Sequential Data via Causal Optimal Transport
We introduce COT-GAN, an adversarial algorithm to train implicit generative models optimized for producing sequential data.
Double Generative Adversarial Networks for Conditional Independence Testing
In this article, we study the problem of high-dimensional conditional independence testing, a key building block in statistics and machine learning.
Variational f-divergence Minimization
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution.
Training generative latent models by variational f-divergence minimization
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific form of f-divergence between the model and data distribution.
