Autoencoder
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). The encoding is validated and refined by attempting to regenerate the input from the encoding. The autoencoder learns a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data (“noise”).
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- enAn autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). The encoding is validated and refined by attempting to regenerate the input from the encoding. The autoencoder learns a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data (“noise”).
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- enMarch 2020
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- Has abstract
- enAn autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). The encoding is validated and refined by attempting to regenerate the input from the encoding. The autoencoder learns a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data (“noise”). Variants exist, aiming to force the learned representations to assume useful properties. Examples are regularized autoencoders (Sparse, Denoising and Contractive), which are effective in learning representations for subsequent classification tasks, and Variational autoencoders, with applications as generative models. Autoencoders are applied to many problems, including facial recognition, feature detection, anomaly detection and acquiring the meaning of words. Autoencoders are also generative models which can randomly generate new data that is similar to the input data (training data).
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- Network
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- Autoencoder
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- enAutoencoder
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- Activation function
- Additive white Gaussian noise
- Alzheimer's disease
- Anomaly detection
- Artificial intelligence
- Artificial neural network
- Backpropagation
- Breast cancer
- Categorical distribution
- Category:Dimension reduction
- Category:Neural network architectures
- Category:Unsupervised learning
- Data compression
- Deep belief network
- Deep learning
- Dimensionality reduction
- Empirical measure
- Face recognition
- Feature learning
- File:Autoencoder schema.png
- File:Autoencoder sparso.png
- File:Autoencoder structure.png
- File:PCA vs Linear Autoencoder.png
- File:Reconstruction autoencoders vs PCA.png
- Frobenius norm
- Generative model
- Geoffrey Hinton
- Gradient descent
- Hash table
- Identity function
- Image compression
- Image denoising
- Information retrieval
- Jacobian matrix and determinant
- JPEG 2000
- Kullback–Leibler divergence
- Language
- Latent variable
- Least squares
- Linguistic
- Machine translation
- Medical imaging
- MRI
- Multilayer perceptron
- Neural machine translation
- Principal component analysis
- Rectified linear unit
- Rectifier (neural networks)
- Relaxation (approximation)
- Representation learning
- Restricted Boltzmann machine
- Russ Salakhutdinov
- Sigmoid function
- Singular value decomposition
- Sparse coding
- Sparse dictionary learning
- Statistical classification
- Super-resolution
- Transformer (machine learning model)
- Unsupervised learning
- Variational autoencoder
- Variational Bayesian methods
- SameAs
- 4wHva
- Autocodificatore
- Autoencoder
- Autoencoder
- Autoencoder
- Autoencoder
- Auto-encodeur
- Bộ tự mã hóa
- m.0grsv6
- Otokodlayıcı
- Q786435
- Автокодировщик
- Автокодувальник
- خودرمزگذار
- オートエンコーダ
- 自编码器
- 오토인코더
- Subject
- Category:Dimension reduction
- Category:Neural network architectures
- Category:Unsupervised learning
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- Autoencoder?oldid=1120107812&ns=0
- WikiPageLength
- 40553
- Wikipage page ID
- 6836612
- Wikipage revision ID
- 1120107812
- WikiPageUsesTemplate
- Template:Differentiable computing
- Template:Distinguish
- Template:Machine learning
- Template:Main
- Template:Noise
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- Template:Short description
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