Conditional Variational Autoencoder

Conditional Variational Autoencoders (CVAEs) are generative models designed to learn complex data distributions while allowing for controlled generation of new samples based on specified conditions. Current research focuses on applying CVAEs to diverse problems, including level generation in games, anomaly detection in time series data, and synthesis of realistic data in scenarios with limited samples, often incorporating techniques like normalizing flows or vector quantization to improve performance. This versatility makes CVAEs a powerful tool across various fields, enabling tasks such as improved data augmentation, uncertainty quantification in predictions, and the creation of more robust and explainable AI models.