variational-autoencoder

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.