mlsanityData to 14 Oct 2026

Autocodificadores Variacionales (VAE) Fundamentos Te'oricos y Aplicaciones

arXiv 2302.09363 ↗First posted 18 Feb 2023cs.AIcited by 0

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Abstract (arXiv)

VAEs are probabilistic graphical models based on neural networks that allow the coding of input data in a latent space formed by simpler probability distributions and the reconstruction, based on such latent variables, of the source data. After training, the reconstruction network, called decoder, is capable of generating new elements belonging to a close distribution, ideally equal to the original one. This article has been written in Spanish to facilitate the arrival of this scientific knowledge to the Spanish-speaking community.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 45 references that are in the map. First posted on arXiv 18 Feb 2023.

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23 of 45 references.

References · 23

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  3. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  4. 2015Variational Inference with Normalizing Flowscited by 2,170
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11 more
  1. 2017InfoVAE: Information Maximizing Variational Autoencoderscited by 124
  2. 2018Learning Disentangled Joint Continuous and Discrete Representationscited by 83
  3. 2016Medical image denoising using convolutional denoising autoencoderscited by 76
  4. 2018Tighter Variational Bounds are Not Necessarily Bettercited by 76
  5. 2016Auxiliary Deep Generative Modelscited by 72
  6. 2015Learning to Generate Images with Perceptual Similarity Metricscited by 39
  7. 2018From Principal Subspaces to Principal Components with Linear Autoencoderscited by 39
  8. 2018Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Modelcited by 35
  9. 2021Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Surveycited by 21
  10. 2016A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Featurescited by 5
  11. 2018AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reductioncited by 5

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