Variational Autoencoder (VAE)
Variational Autoencoder (VAE) is a generative neural network that learns a probabilistic representation of data and uses that representation to generate new examples similar to the data it was trained on.
What is Variational Autoencoder (VAE)?
A VAE consists of an encoder and a decoder. The encoder maps input data to a probability distribution in a latent space rather than a single fixed representation. The decoder then samples from this latent representation and reconstructs the original input or generates new data.
Why is Variational Autoencoder (VAE) Important?
VAEs provide a structured latent space that allows models to generate, reconstruct, and manipulate data. They are useful for learning compact representations and generating new examples while providing a probabilistic approach to modeling complex datasets.
Common use cases
Variational Autoencoders are commonly used for image generation, data compression, anomaly detection, representation learning, synthetic data generation, and generative modeling.