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{{Distinguish|Autocoder|Autocode}}
{{Use dmy dates|date=March 2020|cs1-dates=y}}
[[File:Autoencoder schema.png|thumb|upright=1.15|A schema of an ''autoencoder''. An autoencoder has two main parts: an ''encoder'' that maps the message to a code, and a ''decoder'' that reconstructs the message from the code.]]
{{Machine learning|Artificial neural network}}
An '''autoencoder''' is a type of [[artificial neural network]] used to learn [[Feature learning|efficient codings]] of unlabeled data ([[unsupervised learning]]). 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]], to generate lower-dimensional embeddings for subsequent use by other [[machine learning]] algorithms.<ref>{{Cite book|last1=Bank |first1=Dor |last2=Koenigstein |first2=Noam |last3=Giryes |first3=Raja |year=2023 |chapter=Autoencoders |editor-last1=Rokach |editor-first1=Lior |editor-last2=Maimon |editor-first2=Oded |editor-last3=Shmueli |editor-first3=Erez |title=Machine learning for data science handbook |chapter-url=https://link.springer.com/chapter/10.1007/978-3-031-24628-9_16 |language=en |pages=353–374 |doi=10.1007/978-3-031-24628-9_16|isbn=978-3-031-24627-2 }}</ref>
Variants exist which aim to make the learned representations assume useful properties.<ref name=":0" /> Examples are regularized autoencoders (''sparse'', ''denoising'' and ''contractive'' autoencoders), which are effective in learning representations for subsequent [[Statistical classification|classification]] tasks,<ref name=":4" /> and [[Variational autoencoder|''variational'' autoencoders]], which can be used as [[generative model]]s.<ref name=":11">{{cite journal |arxiv=1906.02691|doi=10.1561/2200000056|bibcode=2019arXiv190602691K|title=An Introduction to Variational Autoencoders|date=2019|last1=Welling|first1=Max|last2=Kingma|first2=Diederik P.|journal=Foundations and Trends in Machine Learning|volume=12|issue=4|pages=307–392|s2cid=174802445}}</ref> Autoencoders are applied to many problems, including [[Facial recognition system|facial recognition]],<ref>Hinton GE, Krizhevsky A, Wang SD. [http://www.cs.toronto.edu/~fritz/absps/transauto6.pdf Transforming auto-encoders.] In International Conference on Artificial Neural Networks 2011 Jun 14 (pp. 44-51). Springer, Berlin, Heidelberg.</ref> [[Feature (computer vision)|feature detection]],<ref name=":2">{{Cite book|last=Géron|first=Aurélien|title=Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow|publisher=O’Reilly Media, Inc.|year=2019|___location=Canada|pages=739–740}}</ref> [[anomaly detection]], and [[Word embedding|learning the meaning of words]].<ref>{{cite journal|doi=10.1016/j.neucom.2008.04.030|title=Modeling word perception using the Elman network|journal=Neurocomputing|volume=71|issue=16–18|pages=3150|date=2008|last1=Liou|first1=Cheng-Yuan|last2=Huang|first2=Jau-Chi|last3=Yang|first3=Wen-Chie|url=http://ntur.lib.ntu.edu.tw//handle/246246/155195 }}</ref><ref>{{cite journal|doi=10.1016/j.neucom.2013.09.055|title=Autoencoder for words|journal=Neurocomputing|volume=139|pages=84–96|date=2014|last1=Liou|first1=Cheng-Yuan|last2=Cheng|first2=Wei-Chen|last3=Liou|first3=Jiun-Wei|last4=Liou|first4=Daw-Ran}}</ref> In terms of [[Synthetic data|data synthesis]], autoencoders can also be used to randomly generate new data that is similar to the input (training) data.<ref name=":2" />
{{Toclimit|3}}
==
=== Definition ===
An autoencoder is defined by the following components: <blockquote>Two sets: the space of decoded messages <math>\mathcal X</math>; the space of encoded messages <math>\mathcal Z</math>. Typically <math>\mathcal X</math> and <math>\mathcal Z</math> are [[Euclidean space]]s, that is, <math>\mathcal X = \R^m, \mathcal Z = \R^n</math> with <math>m > n.</math> </blockquote><blockquote>Two [[parameterization|parametrized]] families of functions: the encoder family <math>E_\phi:\mathcal{X} \rightarrow \mathcal{Z}</math>, parametrized by <math>\phi</math>; the decoder family <math>D_\theta:\mathcal{Z} \rightarrow \mathcal{X}</math>, parametrized by <math>\theta</math>.</blockquote>For any <math>x\in \mathcal X</math>, we usually write <math>z = E_\phi(x)</math>, and refer to it as the code, the [[latent variable]], latent representation, latent vector, etc. Conversely, for any <math>z\in \mathcal Z</math>, we usually write <math>x' = D_\theta(z)</math>, and refer to it as the (decoded) message.
Usually, both the encoder and the decoder are defined as [[multilayer perceptron]]s (MLPs). For example, a one-layer-MLP encoder <math>E_\phi</math> is:
:<math>E_\phi(\mathbf x) = \sigma(Wx+b)</math>
where <math>\sigma</math> is an element-wise [[activation function]], <math>W</math> is a "weight" matrix, and <math>b</math> is a "bias" vector.
=== Training an autoencoder ===
An autoencoder, by itself, is simply a tuple of two functions. To judge its ''quality'', we need a ''task''. A task is defined by a reference probability distribution <math>\mu_{ref}</math> over <math>\mathcal X</math>, and a "reconstruction quality" function <math>d: \mathcal X \times \mathcal X \to [0, \infty]</math>, such that <math>d(x, x')</math> measures how much <math>x'</math> differs from <math>x</math>.
With those, we can define the loss function for the autoencoder as<math display="block">L(\theta, \phi) := \mathbb \mathbb E_{x\sim \mu_{ref}}[d(x, D_\theta(E_\phi(x)))]</math>The ''optimal'' autoencoder for the given task <math>(\mu_{ref}, d)</math> is then <math>\arg\min_{\theta, \phi}L(\theta, \phi)</math>. The search for the optimal autoencoder can be accomplished by any mathematical optimization technique, but usually by [[gradient descent]]. This search process is referred to as "training the autoencoder".
In most situations, the reference distribution is just the [[Empirical measure|empirical distribution]] given by a dataset <math>\{x_1, ..., x_N\} \subset \mathcal X</math>, so that<math display="block">\mu_{ref} = \frac{1}{N}\sum_{i=1}^N \delta_{x_i}</math>
where <math>\delta_{x_i}</math> is the [[Dirac measure]], the quality function is just L2 loss: <math>d(x, x') = \|x - x'\|_2^2</math>, and <math>\|\cdot\|_2</math> is the [[Norm (mathematics)#Euclidean norm|Euclidean norm]]. Then the problem of searching for the optimal autoencoder is just a [[Least squares|least-squares]] optimization:<math display="block">\min_{\theta, \phi} L(\theta, \phi),\qquad \text{where } L(\theta, \phi) = \frac{1}{N}\sum_{i=1}^N \|x_i - D_\theta(E_\phi(x_i))\|_2^2</math>
=== Interpretation ===
An autoencoder has two main parts: an encoder that maps the message to a code, and a decoder that reconstructs the message from the code. An optimal autoencoder would perform as close to perfect reconstruction as possible, with "close to perfect" defined by the reconstruction quality function <math>d</math>.
The simplest way to perform the copying task perfectly would be to duplicate the signal. To suppress this behavior, the code space <math>\mathcal Z</math> usually has fewer dimensions than the message space <math>\mathcal{X}</math>.
Such an autoencoder is called ''undercomplete''. It can be interpreted as [[Data compression|compressing]] the message, or [[Dimensionality reduction|reducing its dimensionality]].<ref name=":12">{{cite journal |last1=Kramer |first1=Mark A. |date=1991 |title=Nonlinear principal component analysis using autoassociative neural networks |url=https://www.researchgate.net/profile/Abir_Alobaid/post/To_learn_a_probability_density_function_by_using_neural_network_can_we_first_estimate_density_using_nonparametric_methods_then_train_the_network/attachment/59d6450279197b80779a031e/AS:451263696510979@1484601057779/download/NL+PCA+by+using+ANN.pdf |journal=AIChE Journal |volume=37 |issue=2 |pages=233–243 |bibcode=1991AIChE..37..233K |doi=10.1002/aic.690370209}}</ref><ref name=":7" />
At the limit of an ideal undercomplete autoencoder, every possible code <math>z</math> in the code space is used to encode a message <math>x</math> that really appears in the distribution <math>\mu_{ref}</math>, and the decoder is also perfect: <math>D_\theta(E_\phi(x)) = x</math>. This ideal autoencoder can then be used to generate messages indistinguishable from real messages, by feeding its decoder arbitrary code <math>z</math> and obtaining <math>D_\theta(z)</math>, which is a message that really appears in the distribution <math>\mu_{ref}</math>.
If the code space <math>\mathcal Z</math> has dimension larger than (''overcomplete''), or equal to, the message space <math>\mathcal{X}</math>, or the hidden units are given enough capacity, an autoencoder can learn the [[identity function]] and become useless. However, experimental results found that overcomplete autoencoders might still [[feature learning|learn useful features]].<ref name="bengio">{{Cite journal |last1=Bengio |first1=Y. |date=2009 |title=Learning Deep Architectures for AI |url=http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf |journal=Foundations and Trends in Machine Learning |volume=2 |issue=8 |pages=1795–7 |citeseerx=10.1.1.701.9550 |doi=10.1561/2200000006 |pmid=23946944|s2cid=207178999 }}</ref>
In the ideal setting, the code dimension and the model capacity could be set on the basis of the complexity of the data distribution to be modeled. A standard way to do so is to add modifications to the basic autoencoder, to be detailed below.<ref name=":0" />
==Variations==
===Variational autoencoder (VAE)===
[[File:VAE Basic.png|thumb|300x300px|The basic scheme of a variational autoencoder. The model receives <math>x</math> as input. The encoder compresses it into the latent space. The decoder receives as input the information sampled from the latent space and produces <math>{x'}</math> as similar as possible to <math>x</math>.]]
{{Main|Variational autoencoder}}
[[Variational autoencoder]]s (VAEs) belong to the families of [[variational Bayesian methods]]. Despite the architectural similarities with basic autoencoders, VAEs are architected with different goals and have a different mathematical formulation. The latent space is, in this case, composed of a mixture of distributions instead of fixed vectors.
Given an input dataset <math>x</math> characterized by an unknown probability function <math>P(x)</math> and a multivariate latent encoding vector <math>z</math>, the objective is to model the data as a distribution <math>p_\theta(x)</math>, with <math>\theta</math> defined as the set of the network parameters so that <math>p_\theta(x) = \int_{z}p_\theta(x,z)dz </math>.
===Sparse autoencoder (SAE)===
Inspired by the [[sparse coding]] hypothesis in neuroscience, ''sparse autoencoders'' (SAE) are variants of autoencoders, such that the codes <math>E_\phi(x)</math> for messages tend to be ''sparse codes'', that is, <math>E_\phi(x)</math> is close to zero in most entries. Sparse autoencoders may include more (rather than fewer) hidden units than inputs, but only a small number of the hidden units are allowed to be active at the same time.<ref name="domingos">{{cite book |last1=Domingos |first1=Pedro |author-link=Pedro Domingos |title=The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World |title-link=The Master Algorithm |date=2015 |publisher=Basic Books |isbn=978-046506192-1 |at="Deeper into the Brain" subsection |chapter=4}}</ref> Encouraging sparsity improves performance on classification tasks.<ref name=":1" /> [[File:Autoencoder sparso.png|thumb|Simple schema of a single-layer sparse autoencoder. The hidden nodes in bright yellow are activated, while the light yellow ones are inactive. The activation depends on the input.]]
There are two main ways to enforce sparsity. One way is to simply clamp all but the highest-k activations of the latent code to zero. This is the '''k-sparse autoencoder'''.<ref name=":1">{{cite arXiv |eprint=1312.5663 |class=cs.LG |first1=Alireza |last1=Makhzani |first2=Brendan |last2=Frey |title=K-Sparse Autoencoders |date=2013}}</ref>
The k-sparse autoencoder inserts the following "k-sparse function" in the latent layer of a standard autoencoder:<math display="block">f_k(x_1, ..., x_n) = (x_1 b_1, ..., x_n b_n)</math>where <math>b_i = 1</math> if <math>|x_i|</math> ranks in the top k, and 0 otherwise.
Backpropagating through <math>f_k</math> is simple: set gradient to 0 for <math>b_i = 0</math> entries, and keep gradient for <math>b_i=1</math> entries. This is essentially a generalized [[Rectifier (neural networks)|ReLU]] function.<ref name=":1" />
The other way is a [[Relaxation (approximation)|relaxed version]] of the k-sparse autoencoder. Instead of forcing sparsity, we add a '''sparsity regularization loss''', then optimize for<math display="block">\min_{\theta, \phi}L(\theta, \phi) + \lambda L_{\text{sparse}} (\theta, \phi)</math>where <math>\lambda > 0</math> measures how much sparsity we want to enforce.<ref name=":6" />
Let the autoencoder architecture have <math>K</math> layers. To define a sparsity regularization loss, we need a "desired" sparsity <math>\hat \rho_k</math> for each layer, a weight <math>w_k</math> for how much to enforce each sparsity, and a function <math>s: [0, 1]\times [0, 1] \to [0, \infty]</math> to measure how much two sparsities differ.
The sparsity loss upon input <math>x</math> for one layer is <math>s(\hat\rho_k, \rho_k(x))</math>, and the sparsity regularization loss for the entire autoencoder is the expected weighted sum of sparsity losses:<math display="block">L_{\text{sparse}}(\theta, \phi) = \mathbb \mathbb E_{x\sim\mu_X}\left[\sum_{k\in 1:K} w_k s(\hat\rho_k, \rho_k(x)) \right]</math>Typically, the function <math>s</math> is either the [[Kullback–Leibler divergence|Kullback-Leibler (KL) divergence]], as<ref name=":1" /><ref name=":6">Ng, A. (2011). [https://web.stanford.edu/class/cs294a/sparseAutoencoder_2011new.pdf Sparse autoencoder]. ''CS294A Lecture notes'', ''72''(2011), 1-19.</ref><ref>{{Cite journal|last1=Nair|first1=Vinod|last2=Hinton|first2=Geoffrey E.|date=2009|title=3D Object Recognition with Deep Belief Nets|url=http://dl.acm.org/citation.cfm?id=2984093.2984244|journal=Proceedings of the 22nd International Conference on Neural Information Processing Systems|series=NIPS'09|___location=USA|publisher=Curran Associates Inc.|pages=1339–1347|isbn=9781615679119}}</ref><ref>{{Cite journal|last1=Zeng|first1=Nianyin|last2=Zhang|first2=Hong|last3=Song|first3=Baoye|last4=Liu|first4=Weibo|last5=Li|first5=Yurong|last6=Dobaie|first6=Abdullah M.|date=2018-01-17|title=Facial expression recognition via learning deep sparse autoencoders|journal=Neurocomputing|volume=273|pages=643–649|doi=10.1016/j.neucom.2017.08.043|issn=0925-2312}}</ref>
::<math>s(\rho, \hat\rho) = KL(\rho || \hat{\rho}) = \rho \log \frac{\rho}{\hat{\rho}}+(1- \rho)\log \frac{1-\rho}{1-\hat{\rho}}</math>
or the L1 loss, as <math>s(\rho, \hat\rho) = |\rho- \hat\rho|</math>, or the L2 loss, as <math>s(\rho, \hat\rho) = |\rho- \hat\rho|^2</math>.
Alternatively, the sparsity regularization loss may be defined without reference to any "desired sparsity", but simply force as much sparsity as possible. In this case, one can define the sparsity regularization loss as <math display="block">L_{\text{sparse}}(\theta, \phi) = \mathbb \mathbb E_{x\sim\mu_X}\left[
\sum_{k\in 1:K} w_k \|h_k\|
\right]</math>where <math>h_k</math> is the activation vector in the <math>k</math>-th layer of the autoencoder. The norm <math>\|\cdot\|</math> is usually the L1 norm (giving the L1 sparse autoencoder) or the L2 norm (giving the L2 sparse autoencoder).
===Denoising autoencoder (DAE)===
[[File:Denoising-autoencoder.png|thumb|A schema of a denoising autoencoder]]
''Denoising autoencoders'' (DAE) try to achieve a ''good'' representation by changing the ''reconstruction criterion''.<ref name=":0" /><ref name=":4" />
A DAE, originally called a "robust autoassociative network" by Mark A. Kramer,<ref name=":13">{{Cite journal |last=Kramer |first=M. A. |date=1992-04-01 |title=Autoassociative neural networks |url=https://dx.doi.org/10.1016/0098-1354%2892%2980051-A |journal=Computers & Chemical Engineering |series=Neutral network applications in chemical engineering |language=en |volume=16 |issue=4 |pages=313–328 |doi=10.1016/0098-1354(92)80051-A |issn=0098-1354|url-access=subscription }}</ref> is trained by intentionally corrupting the inputs of a standard autoencoder during training. A noise process is defined by a probability distribution <math>\mu_T</math> over functions <math>T:\mathcal X \to \mathcal X</math>. That is, the function <math>T</math> takes a message <math>x\in \mathcal X</math>, and corrupts it to a noisy version <math>T(x)</math>. The function <math>T</math> is selected randomly, with a probability distribution <math>\mu_T</math>.
Given a task <math>(\mu_{\text{ref}}, d)</math>, the problem of training a DAE is the optimization problem:<math display="block">\min_{\theta, \phi}L(\theta, \phi) = \mathbb \mathbb E_{x\sim \mu_X, T\sim\mu_T}[d(x, (D_\theta\circ E_\phi \circ T)(x))]</math>That is, the optimal DAE should take any noisy message and attempt to recover the original message without noise, thus the name "denoising"''.''
Usually, the noise process <math>T</math> is applied only during training and testing, not during downstream use.
The use of DAE depends on two assumptions:
* There exist representations to the messages that are relatively stable and robust to the type of noise we are likely to encounter;
* The said representations capture structures in the input distribution that are useful for our purposes.<ref name=":4">{{Cite journal|last1=Vincent|first1=Pascal|last2=Larochelle|first2=Hugo|date=2010|title=Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion|journal=Journal of Machine Learning Research|volume=11|pages=3371–3408}}</ref>
Example noise processes include:
* additive isotropic [[Additive white Gaussian noise|Gaussian noise]],
* masking noise (a fraction of the input is randomly chosen and set to 0)
* salt-and-pepper noise (a fraction of the input is randomly chosen and randomly set to its minimum or maximum value).<ref name=":4" />
=== Contractive autoencoder (CAE) ===
A ''contractive autoencoder'' (CAE) adds the contractive regularization loss to the standard autoencoder loss:<math display="block">\min_{\theta, \phi}L(\theta, \phi) + \lambda L_{\text{cont}} (\theta, \phi)</math>where <math>\lambda > 0</math> measures how much contractive-ness we want to enforce. The contractive regularization loss itself is defined as the expected square of [[Frobenius norm]] of the [[Jacobian matrix and determinant|Jacobian matrix]] of the encoder activations with respect to the input:<math display="block">L_{\text{cont}}(\theta, \phi) = \mathbb E_{x\sim \mu_{ref}} \|\nabla_x E_\phi(x) \|_F^2</math>To understand what <math>L_{\text{cont}}</math> measures, note the fact<math display="block">\|E_\phi(x + \delta x) - E_\phi(x)\|_2 \leq \|\nabla_x E_\phi(x) \|_F \|\delta x\|_2</math>for any message <math>x\in \mathcal X</math>, and small variation <math>\delta x</math> in it. Thus, if <math>\|\nabla_x E_\phi(x) \|_F^2</math> is small, it means that a small neighborhood of the message maps to a small neighborhood of its code. This is a desired property, as it means small variation in the message leads to small, perhaps even zero, variation in its code, like how two pictures may look the same even if they are not exactly the same.
The DAE can be understood as an infinitesimal limit of CAE: in the limit of small Gaussian input noise, DAEs make the reconstruction function resist small but finite-sized input perturbations, while CAEs make the extracted features resist infinitesimal input perturbations.
=== Minimum description length autoencoder (MDL-AE) ===
A ''minimum description length autoencoder'' (MDL-AE) is an advanced variation of the traditional autoencoder, which leverages principles from information theory, specifically the [[Minimum description length|Minimum Description Length (MDL) principle]]. The MDL principle posits that the best model for a dataset is the one that provides the shortest combined encoding of the model and the data. In the context of [[autoencoders]], this principle is applied to ensure that the learned representation is not only compact but also interpretable and efficient for reconstruction.
The MDL-AE seeks to minimize the total description length of the data, which includes the size of the [[latent representation]] (code length) and the error in reconstructing the original data. The objective can be expressed as
<math>L_{\text{code}} + L_{\text{error}}</math>, where <math>L_{\text{code}}</math> represents the length of the compressed latent representation and <math>L_{\text{error}}</math> denotes the reconstruction error.<ref name=":5">{{Cite journal |last1=Hinton |first1=Geoffrey E |last2=Zemel |first2=Richard |date=1993 |title=Autoencoders, Minimum Description Length and Helmholtz Free Energy |url=https://proceedings.neurips.cc/paper/1993/hash/9e3cfc48eccf81a0d57663e129aef3cb-Abstract.html |journal=Advances in Neural Information Processing Systems |publisher=Morgan-Kaufmann |volume=6}}</ref>
The ''concrete autoencoder'' is designed for discrete feature selection.<ref>{{cite arXiv|last1=Abid|first1=Abubakar|last2=Balin|first2=Muhammad Fatih|last3=Zou|first3=James|date=2019-01-27|title=Concrete Autoencoders for Differentiable Feature Selection and Reconstruction|eprint=1901.09346|class=cs.LG}}</ref> A concrete autoencoder forces the latent space to consist only of a user-specified number of features. The concrete autoencoder uses a continuous [[Relaxation (approximation)|relaxation]] of the [[categorical distribution]] to allow gradients to pass through the feature selector layer, which makes it possible to use standard [[backpropagation]] to learn an optimal subset of input features that minimize reconstruction loss.
==Advantages of depth==
[[File:Autoencoder_structure.png|350x350px|Schematic structure of an autoencoder with 3 fully connected hidden layers. The code (z, or h for reference in the text) is the most internal layer.|thumb]]
Autoencoders are often trained with a single
* Depth can exponentially reduce the computational cost of representing some functions.
* Depth can exponentially decrease the amount of training data needed to learn some functions.
* Experimentally, deep autoencoders yield better compression compared to shallow or linear autoencoders.<ref name=":7" />
=== Training ===
[[Geoffrey Hinton]] developed the [[deep belief network]] technique for training many-layered deep autoencoders. His method involves treating each
Researchers have debated whether joint training (i.e. training the whole architecture together with a single global reconstruction objective to optimize) would be better for deep auto-encoders.<ref name=":9">{{cite arXiv |eprint=1405.1380|last1=Zhou|first1=Yingbo|last2=Arpit|first2=Devansh|last3=Nwogu|first3=Ifeoma|last4=Govindaraju|first4=Venu|title=Is Joint Training Better for Deep Auto-Encoders?|class=stat.ML|date=2014}}</ref> A 2015 study showed that joint training learns better data models along with more representative features for classification as compared to the layerwise method.<ref name=":9" /> However, their experiments showed that the success of joint training depends heavily on the regularization strategies adopted.<ref name=":9" /><ref>R. Salakhutdinov and G. E. Hinton, “Deep Boltzmann machines,” in AISTATS, 2009, pp. 448–455.</ref>
== History ==
(Oja, 1982)<ref>{{Cite journal |last=Oja |first=Erkki |date=1982-11-01 |title=Simplified neuron model as a principal component analyzer |url=https://link.springer.com/article/10.1007/BF00275687 |journal=Journal of Mathematical Biology |language=en |volume=15 |issue=3 |pages=267–273 |doi=10.1007/BF00275687 |pmid=7153672 |issn=1432-1416|url-access=subscription }}</ref> noted that [[Principal component analysis | PCA]] is equivalent to a neural network with one hidden layer with identity activation function. In the language of autoencoding, the input-to-hidden module is the encoder, and the hidden-to-output module is the decoder. Subsequently, in (Baldi and Hornik, 1989)<ref name="auto">{{Cite journal |last1=Baldi |first1=Pierre |last2=Hornik |first2=Kurt |date=1989-01-01 |title=Neural networks and principal component analysis: Learning from examples without local minima |url=https://www.sciencedirect.com/science/article/abs/pii/0893608089900142 |journal=Neural Networks |volume=2 |issue=1 |pages=53–58 |doi=10.1016/0893-6080(89)90014-2 |issn=0893-6080|url-access=subscription }}</ref> and (Kramer, 1991)<ref name=":12" /> generalized PCA to autoencoders, a technique which they termed "nonlinear PCA".
Immediately after the resurgence of neural networks in the 1980s, it was suggested in 1986<ref>{{Cite book |last1=Rumelhart |first1=David E. |url=https://direct.mit.edu/books/book/4424/Parallel-Distributed-ProcessingExplorations-in-the |title=Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations |last2=McClelland |first2=James L. |last3=AU |date=1986 |publisher=The MIT Press |isbn=978-0-262-29140-8 |language=en |chapter=2. A General Framework for Parallel Distributed Processing |doi=10.7551/mitpress/5236.001.0001}}</ref> that a neural network be put in "auto-association mode". This was then implemented in (Harrison, 1987)<ref>Harrison TD (1987) A Connectionist framework for continuous speech recognition. Cambridge University Ph. D. dissertation</ref> and (Elman, Zipser, 1988)<ref>{{Cite journal |last1=Elman |first1=Jeffrey L. |last2=Zipser |first2=David |date=1988-04-01 |title=Learning the hidden structure of speech |url=https://pubs.aip.org/jasa/article/83/4/1615/826094/Learning-the-hidden-structure-of-speechLearning |journal=The Journal of the Acoustical Society of America |language=en |volume=83 |issue=4 |pages=1615–1626 |doi=10.1121/1.395916 |pmid=3372872 |bibcode=1988ASAJ...83.1615E |issn=0001-4966|url-access=subscription }}</ref> for speech and in (Cottrell, Munro, Zipser, 1987)<ref>{{Cite journal |last1=Cottrell |first1=Garrison W. |last2=Munro |first2=Paul |last3=Zipser |first3=David |date=1987 |title=Learning Internal Representation From Gray-Scale Images: An Example of Extensional Programming |url=https://escholarship.org/uc/item/2zs7w6z8 |journal=Proceedings of the Annual Meeting of the Cognitive Science Society |language=en |volume=9 }}</ref> for images.<ref name=":14" /> In (Hinton, Salakhutdinov, 2006),<ref name=":72">{{cite journal |last1=Hinton |first1=G. E. |last2=Salakhutdinov |first2=R.R. |date=28 July 2006 |title=Reducing the Dimensionality of Data with Neural Networks |journal=Science |volume=313 |issue=5786 |pages=504–507 |bibcode=2006Sci...313..504H |doi=10.1126/science.1127647 |pmid=16873662 |s2cid=1658773}}</ref> [[deep belief network]]s were developed. These train a pair [[restricted Boltzmann machine]]s as encoder-decoder pairs, then train another pair on the latent representation of the first pair, and so on.<ref name="scholar">{{Cite journal |vauthors=Hinton G |year=2009 |title=Deep belief networks |journal=Scholarpedia |volume=4 |issue=5 |pages=5947 |bibcode=2009SchpJ...4.5947H |doi=10.4249/scholarpedia.5947 |doi-access=free}}</ref>
The first applications of AE date to early 1990s.<ref name=":0" /><ref>{{Cite journal |last=Schmidhuber |first=Jürgen |date=January 2015 |title=Deep learning in neural networks: An overview |journal=Neural Networks |volume=61 |pages=85–117 |arxiv=1404.7828 |doi=10.1016/j.neunet.2014.09.003 |pmid=25462637 |s2cid=11715509}}</ref><ref name=":5" /> Their most traditional application was [[dimensionality reduction]] or [[feature learning]], but the concept became widely used for learning [[generative model]]s of data.<ref name="VAE">{{cite arXiv |eprint=1312.6114 |class=stat.ML |author1=Diederik P Kingma |first2=Max |last2=Welling |title=Auto-Encoding Variational Bayes |date=2013}}</ref><ref name="gan_faces">Generating Faces with Torch, Boesen A., Larsen L. and Sonderby S.K., 2015 {{URL|http://torch.ch/blog/2015/11/13/gan.html}}</ref> Some of the most powerful [[Artificial intelligence|AIs]] in the 2010s involved autoencoder modules as a component of larger AI systems, such as VAE in [[Stable Diffusion]], discrete VAE in Transformer-based image generators like [[DALL-E|DALL-E 1]], etc.
During the early days, when the terminology was uncertain, the autoencoder has also been called identity mapping,<ref name="auto"/><ref name=":12" /> auto-associating,<ref>{{Cite journal |last1=Ackley |first1=D |last2=Hinton |first2=G |last3=Sejnowski |first3=T |date=March 1985 |title=A learning algorithm for boltzmann machines |url=http://doi.wiley.com/10.1016/S0364-0213(85)80012-4 |journal=Cognitive Science |language=en |volume=9 |issue=1 |pages=147–169 |doi=10.1016/S0364-0213(85)80012-4}}</ref> [[self-supervised learning|self-supervised]] [[backpropagation]],<ref name=":12" /> or Diabolo network.<ref>{{Cite journal |last1=Schwenk |first1=Holger |last2=Bengio |first2=Yoshua |date=1997 |title=Training Methods for Adaptive Boosting of Neural Networks |url=https://proceedings.neurips.cc/paper/1997/hash/9cb67ffb59554ab1dabb65bcb370ddd9-Abstract.html |journal=Advances in Neural Information Processing Systems |publisher=MIT Press |volume=10}}</ref><ref name="bengio" />
== Applications ==
The two main applications of autoencoders are [[dimensionality reduction]] and [[information retrieval]] (or [[Content-addressable memory|associative memory]]),<ref name=":0">{{Cite book|url=http://www.deeplearningbook.org|title=Deep Learning|last1=Goodfellow|first1=Ian|last2=Bengio|first2=Yoshua|last3=Courville|first3=Aaron|publisher=MIT Press|date=2016|isbn=978-0262035613}}</ref> but modern variations have been applied to other tasks.
=== Dimensionality reduction ===
[[File:PCA vs Linear Autoencoder.png|thumb|Plot of the first two Principal Components (left) and a two-dimension hidden layer of a Linear Autoencoder (Right) applied to the [[Fashion MNIST
For Hinton's 2006 study,<ref name=":7" /> he pretrained a multi-layer autoencoder with a stack of [[Restricted Boltzmann machine|RBMs]] and then used their weights to initialize a deep autoencoder with gradually smaller hidden layers until hitting a bottleneck of 30 neurons. The resulting 30 dimensions of the code yielded a smaller reconstruction error compared to the first 30 components of a principal component analysis (PCA), and learned a representation that was qualitatively easier to interpret, clearly separating data clusters.<ref name=":0" /><ref name=":7" />
==== Principal component analysis ====
[[File:Reconstruction autoencoders vs PCA.png|thumb|Reconstruction of 28x28pixel images by an Autoencoder with a code size of two (two-units hidden layer) and the reconstruction from the first two Principal Components of PCA. Images come from the [[Fashion MNIST|Fashion MNIST dataset]].<ref name=":10" />]]
If linear activations are used, or only a single sigmoid hidden layer, then the optimal solution to an autoencoder is strongly related to [[principal component analysis]] (PCA).<ref name=":14">{{Cite journal|last1=Bourlard|first1=H.|last2=Kamp|first2=Y.|date=1988|title=Auto-association by multilayer perceptrons and singular value decomposition|journal=Biological Cybernetics|volume=59|issue=4–5|pages=291–294|doi=10.1007/BF00332918|pmid=3196773|s2cid=206775335|url=http://infoscience.epfl.ch/record/82601}}</ref><ref>{{cite book|title=Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB '14|last1=Chicco|first1=Davide|last2=Sadowski|first2=Peter|last3=Baldi|first3=Pierre|date=2014|isbn=9781450328944|pages=533|chapter=Deep autoencoder neural networks for gene ontology annotation predictions|doi=10.1145/2649387.2649442|hdl=11311/964622|s2cid=207217210|url=http://dl.acm.org/citation.cfm?id=2649442}}</ref> The weights of an autoencoder with a single hidden layer of size <math>p</math> (where <math>p</math> is less than the size of the input) span the same vector subspace as the one spanned by the first <math>p</math> principal components, and the output of the autoencoder is an orthogonal projection onto this subspace. The autoencoder weights are not equal to the principal components, and are generally not orthogonal, yet the principal components may be recovered from them using the [[singular value decomposition]].<ref>{{cite
However, the potential of autoencoders resides in their non-linearity, allowing the model to learn more powerful generalizations compared to PCA, and to reconstruct the input with significantly lower information loss.<ref name=":7" />
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=== Anomaly detection ===
Another application for autoencoders is [[anomaly detection]].<ref name=":13" /><ref>{{Cite book |last1=Morales-Forero
Typically, this means that on a validation set the empirical distribution of reconstruction errors is recorded and then (e.g.) the empirical 95-percentile <math>x_p</math> is taken as threshold <math>t:=x_p</math> to flag anomalous data points: <math>\text{loss}(x, \text{reconstruction}(x))>t \implies \text{anomaly}</math>. Since the threshold is an empirical [[quantile]] estimate, there is an inherent difficulty with "correctly" setting this threshold:
In many cases the distribution of the empirical quantile is asymptotically a normal distribution <math>\text{empirical p-quantile} \sim \mathcal{N}\left(\mu=p, \sigma^2=\frac{p( 1 - p )}{n f(x_p)^2}\right),</math> with <math>f(x_p)</math> the probability density at the quantile. This means that the variance grows if an extreme quantile is considered (because <math>f(x_p)</math> is small there). This means that there is a, potentially, a big uncertainty in what is the right choice for the threshold since it is ''estimated'' from a validation set.
Recent literature has however shown that certain autoencoding models can, counterintuitively, be very good at reconstructing anomalous examples and consequently not able to reliably perform anomaly detection.<ref>{{cite arXiv|last1=Nalisnick|first1=Eric|last2=Matsukawa|first2=Akihiro|last3=Teh|first3=Yee Whye|last4=Gorur|first4=Dilan|last5=Lakshminarayanan|first5=Balaji|date=2019-02-24|title=Do Deep Generative Models Know What They Don't Know?|class=stat.ML|eprint=1810.09136}}</ref><ref>{{Cite journal|last1=Xiao|first1=Zhisheng|last2=Yan|first2=Qing|last3=Amit|first3=Yali|date=2020|title=Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder|url=https://proceedings.neurips.cc/paper/2020/hash/eddea82ad2755b24c4e168c5fc2ebd40-Abstract.html|journal=Advances in Neural Information Processing Systems|language=en|volume=33|arxiv=2003.02977}}</ref>
Intuitively, this can be understood by considering those one layer auto encoders which are related to PCA - also in this case there can be perfect rein reconstructions for points which are far away from the data region but which lie on a principal component axis.
It is best to analyze if the anomalies which are flagged by the auto encoder are true anomalies. In this sense all the metrics in [[Evaluation of binary classifiers]] can be considered. The fundamental challenge which comes with the unsupervised (self-supervised) learning setting is, that labels for rare events do not exist (in which case the labels first have to be gathered and the data set will be imbalanced) or anomaly indicating labels are very rare, introducing larger [[confidence interval]]s for these performance estimates.
=== Image processing ===
The characteristics of autoencoders are useful in image processing.
One example can be found in lossy [[image compression]], where autoencoders outperformed other approaches and proved competitive against [[JPEG 2000]].<ref>{{cite
Another useful application of autoencoders in image preprocessing is [[image denoising]].<ref>Cho, K. (2013, February). Simple sparsification improves sparse denoising autoencoders in denoising highly corrupted images. In ''International Conference on Machine Learning'' (pp. 432-440).</ref><ref>{{cite
Autoencoders found use in more demanding contexts such as [[medical imaging]] where they have been used for [[image denoising]]<ref>{{Cite
=== Drug discovery ===
In 2019 molecules generated with variational autoencoders were validated experimentally in mice.<ref>{{cite journal |last1=Zhavoronkov |first1=Alex|s2cid=201716327|date=2019|title=Deep learning enables rapid identification of potent DDR1 kinase inhibitors |journal=Nature Biotechnology |volume=37|issue=9|pages=1038–1040|doi=10.1038/s41587-019-0224-x |pmid=31477924}}</ref><ref>{{cite
=== Popularity prediction ===
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=== Machine translation ===
Autoencoders have been applied to [[machine translation]], which is usually referred to as [[neural machine translation]] (NMT).<ref>{{cite
=== Communication Systems ===
Autoencoders in communication systems are important because they help in encoding data into a more resilient representation for channel impairments, which is crucial for transmitting information while minimizing errors. In Addition, AE-based systems can optimize end-to-end communication performance. This approach can solve the several limitations of designing communication systems such as the inherent difficulty in accurately modeling the complex behavior of real-world channels.<ref>{{cite arXiv |eprint=2412.13843|last1=Alnaseri|first1=Omar|last2=Alzubaidi|first2=Laith|last3=Himeur|first3=Yassine|last4=Timmermann|first4=Jens|title=A Review on Deep Learning Autoencoder in the Design of Next-Generation Communication Systems|class=eess.SP|date=2024}}</ref>
==See also==
* [[Representation learning]]
* [[Singular value decomposition]]
* [[Sparse dictionary learning]]
* [[Deep learning]]
== Further reading ==
* {{cite book |last1=Bank |first1=Dor |title=Machine Learning for Data Science Handbook |last2=Koenigstein |first2=Noam |last3=Giryes |first3=Raja |publisher=Springer International Publishing |year=2023 |isbn=978-3-031-24627-2 |publication-place=Cham |chapter=Autoencoders |doi=10.1007/978-3-031-24628-9_16}}
* {{Cite book |last1=Goodfellow |first1=Ian |title=Deep learning |last2=Bengio |first2=Yoshua |last3=Courville |first3=Aaron |date=2016 |publisher=The MIT press |isbn=978-0-262-03561-3 |series=Adaptive computation and machine learning |___location=Cambridge, Mass |chapter=14. Autoencoders |chapter-url=https://www.deeplearningbook.org/contents/autoencoders.html}}
==References==
{{Reflist|30em}}
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