variational-autoencoder
Expanded definition
Variational Autoencoders (VAEs) are a class of generative models that use neural networks to learn a probabilistic representation of the input data. They combine traditional autoencoders with variational inference to generate new data similar to the training set. A misconception about VAEs is that they produce high-quality samples like GANs; while they can generate diverse samples, the quality may not match that of models specifically designed for image generation like GANs.
Related terms
Explore adjacent ideas in the knowledge graph.
variational-autoencoder FAQ
What is variational-autoencoder?
A generative model that learns to represent data in a latent space using variational inference.
How is variational-autoencoder used in AI systems?
Variational Autoencoders (VAEs) are a class of generative models that use neural networks to learn a probabilistic representation of the input data. They combine traditional autoencoders with variational inference to generate new data similar to the training set. A misconception about VAEs is that they produce high-quality samples like GANs; while they can generate diverse samples, the quality ma...
Related
Comparisons, tools, and models that connect to this idea.