Researchers have developed a novel deep-learning architecture that generates high-quality synthetic images of rare land covers to eliminate bias and drastically improve the accuracy of environmental ...
Researchers at New York University have developed a new architecture for diffusion models that improves the semantic representation of the images they generate. “Diffusion Transformer with ...
In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural ...
This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...
Alibaba’s Qwen team has launched Qwen-Image-Edit, a new open-source AI model that directly challenges professional software like Adobe Photoshop, which is used by over 90% of the world’s creative ...
I am trying to run a minimal autoencoder demo using the original Hunyuan3D-2 model (not the mini version). The original minimal_vae_demo.py uses the following code: vae = ShapeVAE.from_pretrained( ...
Stay up to date with everything that is happening in the wonderful world of AM via our LinkedIn community. Although it is still in development and a bit raw, Spar3D already delivers unprecedented ...
Abstract: The variational autoencoder (VAE) has proven highly effective in monitoring nonlinear stochastic processes, primarily under the assumption of complete and temporally independent data.
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