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.
Transfer Learning is a technique that allows AI models to apply knowledge gained from one task to another related task. By leveraging pre-trained models…