Two related papers explore the theoretical underpinnings of generative models, particularly focusing on stochastic interpolation. The research analyzes how these models behave with finite training data, deriving expressions for optimal fields and score functions. The findings suggest that generated samples are essentially training samples with added noise, with deviations influenced by discretization and estimation errors, leading to new definitions for overfitting and underfitting in generative contexts. AI
IMPACT Provides theoretical definitions for overfitting and underfitting in generative models, potentially guiding future research and development.
RANK_REASON The cluster contains two academic papers discussing theoretical aspects of generative models and their training properties.
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