Introduction
The variational autoencoder establishes a differentiable framework for generative latent-variable modelling. For this genealogy, a significant legacy is the movement of latent space from statistical terminology into visual culture: images can be compressed into representations, reconstructed, and newly sampled within a learned generative space.
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Source · Auto-Encoding Variational Bayes ↗RESEARCH ACCOUNT
Learning a latent space for generation
ANTI-AI ARCHIVE · Revised 2026-10-02
Method and contribution
Kingma and Welling address inference and learning with continuous latent variables when the true posterior is intractable. An approximate inference model and a reparameterised objective make stochastic-gradient optimisation possible. The encoder and generative model form a learned probabilistic relationship, rather than simply shrinking and enlarging pictures.
Section sources: Auto-Encoding Variational Bayes
Art-historical interpretation
Editorial interpretation: latent representations make interpolation and variation available to artists. Distance in that representation need not correspond to perceived stylistic distance. Historical accounts should therefore identify the model and the artist’s selection process instead of treating latent space as an unexplained source of inspiration.
Section sources: Auto-Encoding Variational Bayes
Sources for this account
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Dates & version record
Date displayed for this node: 2013 · Historical date recorded for the source: 2013
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Original sources & further reading
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Auto-Encoding Variational Bayes
https://arxiv.org/abs/1312.6114
Current cited URL
Provenance, translation & verification
Timeline entry #078 · Historical node
Research materials & supplement references · 1
AI Art Genealogy Research Master · p. 15 · 044
Date as recorded: 2013
Auto-Encoding Variational Bayes ↗
Auto-Encoding Variational Bayes
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Cite this node
ANTI-AI ARCHIVE. Auto-Encoding Variational Bayes. Art-history node #078. https://salondesrefuses.cn/en/art-history/044
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