This project implements a powerful Variational Autoencoder (VAE) with Gaussian Mixture as a prior distribution, enabling both unsupervised clustering and high-quality image generation. The model is ...

Our main focus is on using a Variational Autoencoder (VAE) to learn a structured latent space and a Gaussian Mixture Model (GMM) for clustering and classification. Train a Variational Autoencoder (VAE ...

This paper proposes a variational autoencoder with optimizing Gaussian mixture ... and for realizing the VAE with optimizing Gaussian mixture model priors. Compared with the standard VAE method, the ...

After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward ... For both simulated and real datasets, the model can attain an average accuracy ...

A Variational Autoencoder (VAE) in neural networks works similarly ... It's like having an internal model of faces that it can tweak to create new, yet familiar, images. Variational Autoencoders ...

4. Discussion The proposed VAE-SURF model introduces a novel hybrid approach for anomaly detection in crowded scenes by synergistically integrating Variational Autoencoders (VAEs) and Speeded-Up ...