{
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  "headline": "Self-Supervised Learning: Learning Without Labels",
  "description": "Self-supervised learning extracts supervisory signals from unlabeled data, enabling models to learn useful representations without expensive human annotation. SSL underpins modern pretraining of foundation models.",
  "dateCreated": "2026-05-24T02:49:13.660Z",
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  "license": "https://creativecommons.org/licenses/by/4.0/",
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      "name": "A Simple Framework for Contrastive Learning of Visual Representations (SimCLR)",
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