Variational Autoencoders are a type of generative model used in unsupervised learning. VAEs learn a low-dimensional representation of input data and can generate new data samples similar to the training data. They have applications in tasks such as image generation, anomaly detection, and data compression.
Unsupervised Learning is a Machine Learning technique where models learn patterns and structures within data without labelled examples. By uncovering hidden relationships and clustering…