How far can we go with ImageNet for Text-to-Image generation?

• URL: http://arxiv.org/abs/2502.21318v1
• Date: Fri, 28 Feb 2025 18:59:42 GMT
• Title: How far can we go with ImageNet for Text-to-Image generation?
• Authors: L. Degeorge, A. Ghosh, N. Dufour, D. Picard, V. Kalogeiton,
• Abstract summary: Recent text-to-image (T2I) generation models have achieved remarkable results by training on billion-scale datasets.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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