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      "statement": "# AutoAugment and RandAugment image-augmentation policy family\n\nAutoAugment searches operation/probability/magnitude subpolicies using validation rewards; RandAugment replaces the separate proxy search with uniform sampled operations and a small count/magnitude parameter space.\n\n## Scientific scope\n\nThese related original/efficient-extension methods form one family with separate source bylines. Image operations and baseline augmentations are inherited. RandAugment still tunes count and magnitude, typically by grid search, and is not parameter-free. The original AutoAugment v1 has 16 candidate operations including SamplePairing, unlike later variants.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nEkin D. Cubuk; Barret Zoph; Dandelion Mané; Vijay Vasudevan; Quoc V. Le; Jonathon Shlens\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/pdf/1805.09501v1)\n- [RandAugment: Practical data augmentation with no separate search](https://arxiv.org/pdf/1909.13719v1)\n",
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