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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 ...
Schematic structure of an autoencoder with 3 fully connected hidden layers. The code (z, or h for reference in the text) is the most internal layer. Autoencoders are often trained with a single-layer encoder and a single-layer decoder, but using many-layered (deep) encoders and decoders offers many advantages. [2]
The encoder part of the VAE takes an image as input and outputs a lower-dimensional latent representation of the image. This latent representation is then used as input to the U-Net. Once the model is trained, the encoder is used to encode images into latent representations, and the decoder is used to decode latent representations back into images.
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 ]
Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process all the input tokens together one layer after another, while the decoder consists of decoding layers that iteratively process the encoder's output and the decoder's output tokens so far.
The first one ("encoder") takes in image patches with positional encoding, and outputs vectors representing each patch. The second one (called "decoder", even though it is still an encoder-only Transformer) takes in vectors with positional encoding and outputs image patches again. During training, both the encoder and the decoder ViTs are used.
An emergency response team with Washington, DC Fire and EMS make their way to airplane wreckage in the Potomac River near Ronald Reagan Washington Airport on January 30, 2025, in Arlington, Virginia.
Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units) A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.