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In addition to being seen as an autoencoder neural network architecture, variational autoencoders can also be studied within the mathematical formulation of variational Bayesian methods, connecting a neural encoder network to its decoder through a probabilistic latent space (for example, as a multivariate Gaussian distribution) that corresponds ...
The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational inference, variational autoencoders, and stochastic optimization.
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning.They are typically used in complex statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with various sorts of relationships among the three types of random variables, as ...
Encoder–decoder frameworks are based on neural networks that map highly structured input to highly structured output. The approach arose in the context of machine translation , [ 97 ] [ 98 ] [ 99 ] where the input and output are written sentences in two natural languages.
There is an image with a caption saying it is a variational autoencoder, but it is showing just a plain autoencoder. In a different section, there is something described as a "trick", which seems to be the central point that distinguishes autoencoders from variational autoencoders.
Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder. [15] The VAE encoder compresses the image from pixel space to a smaller dimensional latent space , capturing a more fundamental semantic meaning of the image. [ 14 ]
In probability theory, statistics, and machine learning, the continuous Bernoulli distribution [1] [2] [3] is a family of continuous probability distributions parameterized by a single shape parameter (,), defined on the unit interval [,], by: