- 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...
- as
gradient descent.
classical examples include word
embeddings and
autoencoders. Self-supervised
learning has
since been
applied to many
modalities through...
-
conditional text-to-image generation. LDM
consists of a
variational autoencoder (VAE), a
modified U-Net, and a text encoder. The VAE
encoder compresses...
-
machine learning,
particularly in
variational inference,
variational autoencoders, and
stochastic optimization. It
allows for the
efficient com****tion...
- prin****l
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...
