Model Collapse
(wikipedia.org)
Model collapse is a phenomenon where machine learning models gradually degrade due to errors coming from uncurated training on the outputs of another model, including prior versions of itself.[1][2][3][4] Such outputs are known as synthetic data.
Model collapse is a phenomenon where machine learning models gradually degrade due to errors coming from uncurated training on the outputs of another model, including prior versions of itself.[1][2][3][4] Such outputs are known as synthetic data.