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

Time Series Analysis is an AI technique that analyses data points collected over time. This approach involves detecting trends, patterns, and seasonality in the…
Cognitive Computing involves creating computer systems that can imitate human cognitive abilities, such as perception, reasoning, learning, and problem-solving. By combining AI, Machine Learning,…
Support Vector Machines (SVMs) are Machine Learning algorithms used for classification and regression tasks. SVMs create decision boundaries and maximise the margin between different…
Adversarial machine learning involves studying and defending AI models against attacks or adversarial examples designed to deceive the system. By understanding vulnerabilities and deploying…