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Is synthetic data from generative models ready for image recognition?

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arxiv 2210.07574 v2 pith:N5JSKTAI submitted 2022-10-14 cs.CV

Ruifei He , Shuyang Sun , Xin Yu , Chuhui Xue , Wenqing Zhang , Philip Torr , Song Bai , Xiaojuan Qi This is my paper · ORCID
classification cs.CV
keywords syntheticdatamodelsrecognitionimagestasksgeneratedgeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applicable these generated images are for recognition tasks remains under-explored. In this work, we extensively study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks, and focus on two perspectives: synthetic data for improving classification models in data-scarce settings (i.e. zero-shot and few-shot), and synthetic data for large-scale model pre-training for transfer learning. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks. Code: https://github.com/CVMI-Lab/SyntheticData.

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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