Synthetic datasets created via diffusion models, GAN editing, and pseudo-labeling can substitute for or augment real data to improve facial expression recognition while respecting privacy constraints.
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On Applicability of Synthetic Datasets for Facial Expression Recognition
Synthetic datasets created via diffusion models, GAN editing, and pseudo-labeling can substitute for or augment real data to improve facial expression recognition while respecting privacy constraints.