IEEE

CVA 2 E: A Conditional Variational Autoencoder With an Adversarial Training Process for Hyperspectral Imagery Classification

Abstract: Deep generative models such as the generative adversarial network (GAN) and the variational autoencoder (VAE) have obtained increasing attention in a wide variety of applications.
Hosted on MSN

Smarter search for fuel-cell catalysts uses machine learning

A computational method combining generative AI with atomistic simulations can identify promising platinum alloy catalyst structures for hydrogen fuel cells, report researchers from Science Tokyo.
GitHub

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

In our recent paper, we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. Several recent end-to-end text-to-speech (TTS) models enabling single ...
IEEE

UIE-DDPM: Underwater Image Enhancement Based on the Integration of Physical Model and Conditional Denoising Diffusion Probabilistic Model

Abstract: Underwater image enhancement (UIE) is crucial for underwater perception tasks but is challenged by complex physical degradations, including backscatter, wavelength-dependent attenuation, ...
GitHub

MAGI-1: Autoregressive Video Generation at Scale

We present MAGI-1, a world model that generates videos by autoregressively predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames. Trained to denoise per-chunk ...