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 generative
data augmentation, helping to improve challenging discriminative tasks? We show
that large-scale text-to image diffusion models can be fine-tuned to produce
class conditional models with SOTA FID (1.76 at 256x256 resolution) and
Inception Score (239 at 256x256). The model also yields a new SOTA in
Classification Accuracy Scores (64.96 for 256x256 generative samples, improving
to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with
samples from the resulting models yields significant improvements in ImageNet
classification accuracy over strong ResNet and Vision Transformer baselines.

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。