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.
Supervised Learning is a Machine Learning approach where models are trained using labelled data, with both input and output pairs. By learning from the…