Neural network that learns efficient data encoding in an unsupervised manner
Not to be confused with Autocoder or Autocode.
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An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).[1][2] An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction.
Variants exist, aiming to force the learned representations to assume useful properties.[3] Examples are regularized autoencoders (Sparse, Denoising and Contractive), which are effective in learning representations for subsequent classification tasks,[4] and Variational autoencoders, with applications as generative models.[5] Autoencoders are applied to many problems, including facial recognition,[6] feature detection,[7] anomaly detection and acquiring the meaning of words.[8][9] Autoencoders are also generative models which can randomly generate new data that is similar to the input data (training data).[7]
^Kramer, Mark A. (1991). "Nonlinear principal component analysis using autoassociative neural networks" (PDF). AIChE Journal. 37 (2): 233–243. Bibcode:1991AIChE..37..233K. doi:10.1002/aic.690370209.
^Kramer, M. A. (1992-04-01). "Autoassociative neural networks". Computers & Chemical Engineering. Neutral network applications in chemical engineering. 16 (4): 313–328. doi:10.1016/0098-1354(92)80051-A. ISSN 0098-1354.
^Cite error: The named reference :0 was invoked but never defined (see the help page).
^Cite error: The named reference :4 was invoked but never defined (see the help page).
^Welling, Max; Kingma, Diederik P. (2019). "An Introduction to Variational Autoencoders". Foundations and Trends in Machine Learning. 12 (4): 307–392. arXiv:1906.02691. Bibcode:2019arXiv190602691K. doi:10.1561/2200000056. S2CID 174802445.
^Hinton GE, Krizhevsky A, Wang SD. Transforming auto-encoders. In International Conference on Artificial Neural Networks 2011 Jun 14 (pp. 44-51). Springer, Berlin, Heidelberg.
^ abGéron, Aurélien (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Canada: O’Reilly Media, Inc. pp. 739–740.
^Liou, Cheng-Yuan; Huang, Jau-Chi; Yang, Wen-Chie (2008). "Modeling word perception using the Elman network". Neurocomputing. 71 (16–18): 3150. doi:10.1016/j.neucom.2008.04.030.
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...
algorithm". An adversarial autoencoder (AAE) is more autoencoder than GAN. The idea is to start with a plain autoencoder, but train a discriminator to...
of the traditional Transformer Architecture is also used. In Masked Autoencoder, there are two ViTs put end-to-end. The first one takes in image patches...
as gradient descent. Classical examples include word embeddings and autoencoders. SSL has since been applied to many modalities through the use of deep...
Variational autoencoder These are inspired by Helmholtz machines and combines probability network with neural networks. An Autoencoder is a 3-layer CAM...
store and retrieve multidimensional aperiodic signals. An oscillatory autoencoder has also been demonstrated, which uses a combination of oscillators and...
NSynth (a portmanteau of "Neural Synthesis") is a WaveNet-based autoencoder for synthesizing audio, outlined in a paper in April 2017. The model generates...
In artificial neural network, examples include variational autoencoder, denoising autoencoder, Hopfield network. In reference to computer memory, the idea...
Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization and various forms of clustering. Manifold learning...
and to sparse coding models used in deep learning algorithms such as autoencoder. The simplest training algorithm for vector quantization is: Pick a sample...
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...
approach to nonlinear dimensionality reduction is through the use of autoencoders, a special kind of feedforward neural networks with a bottleneck hidden...
classical music. According to the June 2018 paper Disentangled Sequential Autoencoder, DeepMind has successfully used WaveNet for audio and voice "content...
(instead of emitting a target value). Therefore, autoencoders are unsupervised learning models. An autoencoder is used for unsupervised learning of efficient...
similarity, recommendation systems, and face recognition. Variational Autoencoders (VAEs): VAEs are generative models that simultaneously learn to encode...
detection using transferred generative adversarial networks based on deep autoencoders". Information Sciences. 460–461: 83–102. doi:10.1016/j.ins.2018.04.092...
recognition algorithms and artificial neural networks such as variational autoencoders (VAEs) and generative adversarial networks (GANs). In turn the field...
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