Variational Autoencoders

Variational Autoencoders (VAEs) are generative models that learn a compressed, latent representation of data, aiming to reconstruct the original data from this representation while also learning the underlying data distribution. Current research focuses on improving VAE architectures for specific tasks, such as image generation and anomaly detection, exploring variations like conditional VAEs, hierarchical VAEs, and those incorporating techniques like vector quantization or diffusion models to enhance performance and interpretability. This work is significant because VAEs offer a powerful framework for unsupervised learning, enabling applications in diverse fields ranging from image processing and molecular design to anomaly detection and causal inference.