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| Variationaler Autoencoder× | Autoencoder× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2014 | 2006 |
| Urheber≠ | Kingma, D. P. & Welling, M. | Hinton, G.E. & Salakhutdinov, R.R. |
| Typ≠ | Deep generative latent-variable model (encoder–decoder) | Neural network (encoder-decoder) |
| Wegweisende Quelle≠ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ | Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507. DOI ↗ |
| Aliasnamen | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model | Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. | An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data. |
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