Synthetic Data from Diffusion Models Improves ImageNet Classification
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, David J. Fleet
论文：https://arxiv.org/pdf/2304.08466.pdf
深度生成模型正变得越来越强大，现在可以在文本提示下生成各种高保真的照片现实样本。他们是否已经达到了这样的程度：自然图像的模型可以用于生成数据的增强，帮助改善具有挑战性的判别任务？我们表明，大规模的文本到图像扩散模型可以被微调，以产生具有SOTA FID（256×256分辨率下为1.76）和Inception Score（256×256分辨率下为239）的类条件模型。
该模型在分类精度分数上也产生了新的SOTA（256×256生成样本为64.96，1024×1024样本提高到69.24）。用产生的模型的样本来增强ImageNet训练集，在ImageNet分类精度上比强大的ResNet和Vision Transformer基线有了明显的改善。
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