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A variational autoencoder is a generative model with a prior and noise distribution respectively. Usually such models are trained using the expectation-maximization meta-algorithm (e.g. probabilistic PCA , (spike & slab) sparse coding).
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.
Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder. [17] The VAE encoder compresses the image from pixel space to a smaller dimensional latent space , capturing a more fundamental semantic meaning of the image. [ 16 ]
The discriminator (usually a convolutional network, but other networks are allowed) attempts to decide if an image is an original real image, or a reconstructed image by the ViT. The idea is essentially the same as vector quantized variational autoencoder (VQVAE) plus generative adversarial network (GAN).
Michael Irwin Jordan ForMemRS [6] (born February 25, 1956) is an American scientist, professor at the University of California, Berkeley, research scientist at the Inria Paris, and researcher in machine learning, statistics, and artificial intelligence.
Michael R. Berthold is a German computer scientist, entrepreneur, academic and author. He held the chair for bioinformatics and information mining at Konstanz University , and is an honorary professor at Óbuda University . [ 1 ]
Michael John Wooldridge (born 26 August 1966) is a professor of computer science at the University of Oxford. His main research interests is in multi-agent systems , and in particular, in the computational theory aspects of rational action in systems composed of multiple self-interested agents.