schematic (IMAGE)
Caption
Richard Baraniuk and his team at Rice University studied three variations of self-consuming training loops designed to provide a realistic representation of how real and synthetic data are combined into training datasets for generative models. Schematic illustrates the three training scenarios, i.e. a fully synthetic loop, a synthetic augmentation loop (synthetic + fixed set of real data), and a fresh data loop (synthetic + new set of real data).
Credit
(Image courtesy of Digital Signal Processing Group/Rice University)
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Must credit Digital Signal Processing Group/Rice University.
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Original content