Empirical analysis demonstrates that accumulating data prevents model collapse in generative models, highlighting its importance.
Key Points
Model collapse occurs when performance degrades across iterations, leading to useless outputs with generative models.
In experiments, accumulating data kept test error from increasing indefinitely, unlike when data was replaced over time.
Analysis reveals accumulating datasets leads to finite upper bounds on test errors, significantly improving model reliability over iterations. Both theoretical and empirical evidence supports accumulation as essential for effective training strategies.