{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/kb-2026-00279",
  "headline": "Autoencoders",
  "description": "Autoencoders are unsupervised neural networks that learn compressed representations by reconstructing input: Encoder → compressed latent space → Decoder → reconstruction. They learn the most salient features by forcing data through a bottleneck. Applications: dimensionality reduction, denoising, anomaly detection.",
  "dateCreated": "2026-05-22T14:59:47.483Z",
  "dateModified": "2026-05-22T14:59:47.483Z",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "human_only",
  "citation": []
}