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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.
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 ]
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.
The original BERT paper published results demonstrating that a small amount of finetuning (for BERT LARGE, 1 hour on 1 Cloud TPU) allowed it to achieved state-of-the-art performance on a number of natural language understanding tasks: [1] GLUE (General Language Understanding Evaluation) task set (consisting of 9 tasks);
This is a problem in the calculus of variations, thus it is called the variational method. Since there are not many explicitly parametrized distribution families (all the classical distribution families, such as the normal distribution, the Gumbel distribution, etc, are far too simplistic to model the true distribution), we consider implicitly ...
A 2019 paper proposed the noise conditional score network (NCSN) or score-matching with Langevin dynamics (SMLD). [7] The paper was accompanied by a software package written in PyTorch release on GitHub. [8] A 2020 paper [9] proposed the Denoising Diffusion Probabilistic Model (DDPM), which improves upon the previous method by variational ...
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).