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...

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

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...

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

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

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

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

system can be visualized as a neural network, similar in spirit to an autoencoder, of architecture linear-linear-softmax, as depicted in the diagram. The...

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...

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

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

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

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...

detection RANSAC k-NN Local outlier factor Isolation forest Neural networks Autoencoder Deep learning Feedforward neural network Recurrent neural network LSTM...

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

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

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

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

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

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

data analysis Surrogate data Generative adversarial network Variational autoencoder Data pre-processing Convolutional neural network Regularization (mathematics)...

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

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...

detection RANSAC k-NN Local outlier factor Isolation forest Neural networks Autoencoder Deep learning Feedforward neural network Recurrent neural network LSTM...