Variational Autoencoders (VAEs) for High-Quality Synthetic Data

Glossary/V/Variational Autoencoder (VAE)

What is Variational Autoencoder (VAE)?

A Variational Autoencoder (VAE) is a type of generative model in machine learning that combines elements of both autoencoders and probabilistic modeling. VAEs are used for unsupervised learning tasks, particularly in generating new data samples that resemble the training data.

At its core, a VAE consists of an encoder and a decoder. The encoder takes an input data point and maps it to a latent space representation. This latent space is a lower-dimensional representation that captures the key features and variations in the data. The encoder network learns to encode the input data into the latent space by applying various transformations and reducing the dimensionality.

Related terms

Probablistic Modeling

Probabilistic modeling is a powerful approach in machine learning and statistics that enables us to ...

Generative AI

Generative AI refers to a branch of artificial intelligence (AI) that focuses on creating AI systems...

Not to be confused with:

Probablistic Modeling

Probabilistic modeling is a powerful approach in machine learning and statistics that enables us to ...

Generative AI

Generative AI refers to a branch of artificial intelligence (AI) that focuses on creating AI systems...

Probablistic Modeling

Probabilistic modeling is a powerful approach in machine learning and statistics that enables us to ...

Generative AI

Generative AI refers to a branch of artificial intelligence (AI) that focuses on creating AI systems...

Probablistic Modeling

Probabilistic modeling is a powerful approach in machine learning and statistics that enables us to ...

Generative AI

Generative AI refers to a branch of artificial intelligence (AI) that focuses on creating AI systems...

The decoder, on the other hand, takes a point from the latent space and reconstructs it back into the original input space. The decoder network learns to decode the latent space representation and generate output that closely resembles the original data. The objective of the VAE is to train the encoder and decoder in such a way that the reconstructed output closely matches the input data.

By training a VAE on a specific dataset, it learns to capture the underlying patterns and variations in the data. This enables it to generate new samples that resemble the training data but exhibit novel characteristics. VAEs have been successfully applied to various tasks, including image generation, text generation, and data augmentation.

As research in VAEs progresses, addressing these challenges and exploring new variations of the model will unlock further potential. VAEs continue to contribute to the field of generative modeling, enabling machines to learn, understand, and create data that resembles the complexities of the real world.

Back to glossary