{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/few-shot-learning",
  "headline": "Few-Shot Learning: Prototypical Networks, MAML, and In-Context Adaptation",
  "description": "Few-shot learning teaches AI to recognize new concepts from just a handful of examples -- the way humans learn (see one panda, recognize all pandas). From Prototypical Networks to MAML to in-context learning in foundation models, the ability to generalize from few examples is transforming AI from narrow specialists to flexible generalists.",
  "dateCreated": "2026-05-24T02:49:13.606Z",
  "dateModified": "2026-05-24",
  "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": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Prototypical Networks for Few-shot Learning",
      "sameAs": "https://arxiv.org/abs/1703.05175"
    },
    {
      "@type": "CreativeWork",
      "name": "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (MAML)",
      "sameAs": "https://arxiv.org/abs/1703.03400"
    }
  ]
}