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Apr, 2023
扩散模型生成的合成数据提升 ImageNet 分类准确性
Synthetic Data from Diffusion Models Improves ImageNet Classification
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Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, David J. Fleet
TL;DR
本研究使用大规模的文本到图像扩散模型对分类条件模型进行微调，进而在 ImageNet 分类准确性得分上实现了显著的提升，证明了利用自然图像模型进行生成数据增强的可行性。
Abstract
deep generative models
are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts. Have they reached the point where models of natural images can be used for
gene
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