Conditioning a pretrained diffusion model on augmented real images and class labels produces synthetic training data that improves downstream classification accuracy without any generator fine-tuning.
Dropout: A simple way to prevent neural networks from overfitting
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Augmented Conditioning Is Enough For Effective Training Image Generation
Conditioning a pretrained diffusion model on augmented real images and class labels produces synthetic training data that improves downstream classification accuracy without any generator fine-tuning.