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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.
LDM consists of a variational autoencoder (VAE), a modified U-Net, and a text encoder. The VAE encoder compresses the image from pixel space to a smaller dimensional latent space, capturing a more fundamental semantic meaning of the image. Gaussian noise is iteratively applied to the compressed latent representation during forward diffusion.
An autoencoder has two main parts: an encoder that maps the message to a code, and a decoder that reconstructs the message from the code. An optimal autoencoder would perform as close to perfect reconstruction as possible, with "close to perfect" defined by the reconstruction quality function d {\displaystyle d} .
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 ]
Example of a junction tree. The junction tree algorithm (also known as 'Clique Tree') is a method used in machine learning to extract marginalization in general graphs.In essence, it entails performing belief propagation on a modified graph called a junction tree.
In 1832, Jonathan the Seychelles giant tortoise (Aldabrachelys gigantea hololissa) was born. Although his exact birthdate is unknown, he has been given the official birthday of December 4, 1832.
The regularization parameter plays a critical role in the denoising process. When =, there is no smoothing and the result is the same as minimizing the sum of squares.As , however, the total variation term plays an increasingly strong role, which forces the result to have smaller total variation, at the expense of being less like the input (noisy) signal.