What is Variational Autoencoders (VAE)?

Skill Level:

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.

Other Definitions

Decision Trees are Machine Learning models that use a branching structure to make decisions or predictions. By determining the most important features and creating…
Federated Learning is a privacy-preserving technique where AI models are trained across multiple decentralised devices or systems without sharing raw data. Instead, only aggregated…
istributed Computing refers to the use of multiple computers or servers to perform computational tasks in a networked environment. It enables businesses to process…
Zero-Shot Learning is an AI approach that enables models to learn to recognise new classes or concepts without explicit training examples. This is achieved…