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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).
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 ]
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
This can be understood as a "decoding" process, whereby every latent vector is a code for an image , and the generator performs the decoding. This naturally leads to the idea of training another network that performs "encoding", creating an autoencoder out of the encoder-generator pair.
Code Chord type Major: Major chord: Minor: Minor chord: Augmented: Augmented chord: Diminished: ... List of musical chords Name Chord on C Sound # of p.c.-Forte # p.c ...
It has a relatively large codebase; as of March 10, 2017, the source includes over 600,000 lines of C++, 140,000 lines of Scheme, and 120,000 lines of Python code. [ 13 ] It uses a simple text notation for music input, which LilyPond interprets and processes in a series of stages. [ 14 ]
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 ...