A Note on Shumailov et al. (2024): `AI Models Collapse When Trained on Recursively Generated Data'
This addresses a critical issue for AI researchers and practitioners, as it highlights potential limitations in data reuse strategies, though it is incremental by building on prior work.
The paper investigates the phenomenon of model collapse when generative models are trained on recursively generated data, finding that it is a statistical phenomenon that may be unavoidable.
The study conducted by Shumailov et al. (2024) demonstrates that repeatedly training a generative model on synthetic data leads to model collapse. This finding has generated considerable interest and debate, particularly given that current models have nearly exhausted the available data. In this work, we investigate the effects of fitting a distribution (through Kernel Density Estimation, or KDE) or a model to the data, followed by repeated sampling from it. Our objective is to develop a theoretical understanding of the phenomenon observed by Shumailov et al. (2024). Our results indicate that the outcomes reported are a statistical phenomenon and may be unavoidable.