Variational Autoencoder

Variational Autoencoders (VAEs) are generative models aiming to learn a compressed, lower-dimensional representation (latent space) of input data, allowing for both data reconstruction and generation of new samples. Current research focuses on improving VAE architectures, such as incorporating beta-VAEs for better disentanglement of latent features, and integrating them with other techniques like large language models, vision transformers, and diffusion models to enhance performance in specific applications. This versatility makes VAEs valuable across diverse fields, including image processing, anomaly detection, materials science, and even astrodynamics, by enabling efficient data analysis, feature extraction, and generation of synthetic data where real data is scarce or expensive to obtain.