title: How far can we go with ImageNet for Text-to-Image generation?
summary: Recent text-to-image (T2I) generation models have achieved remarkable results
  by training on billion-scale datasets, following a `bigger is better' paradigm
  that prioritizes data quantity over quality. We challenge this established
  paradigm by demonstrating that strategic data augmentation of small,
  well-curated datasets can match or outperform models trained on massive
  web-scraped collections. Using only ImageNet enhanced with well-designed text
  and image augmentations, we achieve a +2 overall score over SD-XL on GenEval
  and +5 on DPGBench while using just 1/10th the parameters and 1/1000th the
  training images. Our results suggest that strategic data augmentation, rather
  than massive datasets, could offer a more sustainable path forward for T2I
  generation.
paper_authors._id: 67c5c13ca10c7059c3d3d4c9
paper_authors.name: L. Degeorge
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-03T16:07:10.195Z
paper_authors._id: 63bb08b07fd5e883e13efd32
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63bb08b07fd5e883e13efd32/aKAR8alYsYteEQImBrWO7.jpeg
paper_authors.fullname: Lucas Degeorge
paper_authors.type: user
paper_authors.user: Lucasdegeorge
paper_authors._id: 67c5c13ca10c7059c3d3d4ca
paper_authors.name: A. Ghosh
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-03T18:07:11.151Z
paper_authors._id: 66f971c83d94062a4aa808ef
paper_authors.avatarUrl: /avatars/f1d6c4d85d20fd4a614278ecd784c772.svg
paper_authors.fullname: Arijit Ghosh
paper_authors.type: user
paper_authors.user: arijitghosh
paper_authors._id: 67c5c13ca10c7059c3d3d4cb
paper_authors.name: N. Dufour
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-03T16:07:14.366Z
paper_authors._id: 630652803aed65d34e98eee3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/630652803aed65d34e98eee3/XG_PuVFA6ziGQZd3UUZSF.jpeg
paper_authors.fullname: Nicolas Dufour
paper_authors.type: user
paper_authors.user: nicolas-dufour
paper_authors._id: 67c5c13ca10c7059c3d3d4cc
paper_authors.name: D. Picard
paper_authors._id: 67c5c13ca10c7059c3d3d4cd
paper_authors.name: V. Kalogeiton
paper_title: How far can we go with ImageNet for Text-to-Image generation?
paper_summary: Recent text-to-image (T2I) generation models have achieved remarkable results
  by training on billion-scale datasets, following a `bigger is better' paradigm
  that prioritizes data quantity over quality. We challenge this established
  paradigm by demonstrating that strategic data augmentation of small,
  well-curated datasets can match or outperform models trained on massive
  web-scraped collections. Using only ImageNet enhanced with well-designed text
  and image augmentations, we achieve a +2 overall score over SD-XL on GenEval
  and +5 on DPGBench while using just 1/10th the parameters and 1/1000th the
  training images. Our results suggest that strategic data augmentation, rather
  than massive datasets, could offer a more sustainable path forward for T2I
  generation.
paper_ai_summary: Strategic data augmentation of small, well-curated datasets can achieve competitive performance in text-to-image generation with significantly fewer parameters and training images compared to large-scale models.
paper_ai_keywords: text-to-image generation ImageNet data augmentation SD-XL GenEval DPGBench