An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns...

In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It...

CNN. The masked autoencoder (2022) extended ViT to work with unsupervised training. The vision transformer and the masked autoencoder, in turn, stimulated...

machine learning, particularly in variational inference, variational autoencoders, and stochastic optimization. It allows for the efficient computation...

conditional text-to-image generation. LDM consists of a variational autoencoder (VAE), a modified U-Net, and a text encoder. The VAE encoder compresses...

algorithm". An adversarial autoencoder (AAE) is more autoencoder than GAN. The idea is to start with a plain autoencoder, but train a discriminator to...

as gradient descent. Classical examples include word embeddings and autoencoders. Self-supervised learning has since been applied to many modalities through...

and to sparse coding models used in deep learning algorithms such as autoencoder. The simplest training algorithm for vector quantization is: Pick a sample...

principal component analysis (PCA), Boltzmann machine learning, and autoencoders. After the rise of deep learning, most large-scale unsupervised learning...

often achieved using autoencoders, which are a type of neural network architecture used for representation learning. Autoencoders consist of an encoder...

NSynth (a portmanteau of "Neural Synthesis") is a WaveNet-based autoencoder for synthesizing audio, outlined in a paper in April 2017. The model generates...

store and retrieve multidimensional aperiodic signals. An oscillatory autoencoder has also been demonstrated, which uses a combination of oscillators and...

to high-dimensional space. Although the idea of autoencoders is quite old, training of deep autoencoders has only recently become possible through the use...

such as the wake-sleep algorithm. They are a precursor to variational autoencoders, which are instead trained using backpropagation. Helmholtz machines...

approach to nonlinear dimensionality reduction is through the use of autoencoders, a special kind of feedforward neural networks with a bottleneck hidden...

representation for downstream applications such as facial recognition. The autoencoders similarly learn a latent representation of data for later downstream...

procedure of granting degrees based on work experience in France Variational autoencoder, an artificial neural network architecture All pages with titles beginning...

k-NN Local outlier factor Isolation forest Artificial neural network Autoencoder Deep learning Feedforward neural network Recurrent neural network LSTM...

Malla, D.B. & Sogabe, T. 2019, Convolution filter embedded quantum gate autoencoder, Cornell University Library, arXiv.org, Ithaca. Chiu, Ching-Kai; Teo...

we could branch off towards the development of an importance-weighted autoencoder, but we will instead continue with the simplest case with N = 1 {\displaystyle...

the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec...

recognition algorithms and artificial neural networks such as variational autoencoders (VAEs) and generative adversarial networks (GANs). In turn, the field...

(instead of emitting a target value). Therefore, autoencoders are unsupervised learning models. An autoencoder is used for unsupervised learning of efficient...

contrast, many alternative generative modeling methods such as variational autoencoder (VAE) and generative adversarial network do not explicitly represent...

Examples include dictionary learning, independent component analysis, autoencoders, matrix factorisation and various forms of clustering. Manifold learning...

previously-introduced DRAW architecture (which used a recurrent variational autoencoder with an attention mechanism) to be conditioned on text sequences. Images...

high-fidelity audio. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are being used more and more in new audio texture synthesis and...

recursive autoencoders. The main concept is to produce a vector representation of a sentence and its components by recursively using an autoencoder. The vector...

detection using transferred generative adversarial networks based on deep autoencoders" (PDF). Information Sciences. 460–461: 83–102. doi:10.1016/j.ins.2018...

of generative modeling. In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep...