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Generative models analyzed for finite data, overfitting

By PulseAugur Editorial · [3 sources] ·

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.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Generative models analyzed for finite data, overfitting

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The cluster contains two academic papers discussing theoretical aspects of generative models and their training properties.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Yunchen Li, Shaohui Lin, Zhou Yu ·

    Generation Properties of Stochastic Interpolation under Finite Training Set

    arXiv:2509.21925v2 Announce Type: replace-cross Abstract: This paper investigates the theoretical behavior of generative models under finite training populations. Within the stochastic interpolation generative framework, we derive closed-form expressions for the optimal velocity …

  2. arXiv cs.LG TIER_1 English(EN) · Yunchen Li, Shaohui Lin, Zhou Yu ·

    A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models

    arXiv:2606.08554v1 Announce Type: new Abstract: This paper provides a theoretical account of memorization in stochastic interpolation models. By leveraging closed-form expressions for the optimal velocity field and the associated score function, we show that, in the continuous-ti…

  3. arXiv cs.LG TIER_1 English(EN) · Zhou Yu ·

    A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models

    This paper provides a theoretical account of memorization in stochastic interpolation models. By leveraging closed-form expressions for the optimal velocity field and the associated score function, we show that, in the continuous-time oracle setting, both deterministic and stocha…

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