Autoencoder
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Autoencoder
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns...
Variational autoencoder
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling in...
Vision transformer
CNN. The masked autoencoder (2022) extended ViT to work with unsupervised training. The vision transformer and the masked autoencoder, in turn, stimulated...
Generative adversarial network
algorithm". An adversarial autoencoder (AAE) is more autoencoder than GAN. The idea is to start with a plain autoencoder, but train a discriminator to...
Reparameterization trick
machine learning, particularly in variational inference, variational autoencoders, and stochastic optimization. It allows for the efficient computation...
Text-to-image model
latent space rather than directly in pixel space. An autoencoder (often a variational autoencoder (VAE)) is used to convert between pixel space and this...
Kling AI
which has been enhanced by Kuaishou with a self-developed 3D variational autoencoder (VAE) network. This 3D VAE network allows for synchronous spatiotemporal...
Unsupervised learning
principal component analysis (PCA), Boltzmann machine learning, and autoencoders. After the rise of deep learning, most large-scale unsupervised learning...
Feature learning
as gradient descent. Classical examples include word embeddings and autoencoders. Self-supervised learning has since been applied to many modalities through...
Oscillatory neural network
store and retrieve multidimensional aperiodic signals. An oscillatory autoencoder has also been demonstrated, which uses a combination of oscillators and...