Generalization and Memorization: The Bias Potential Model

• URL: http://arxiv.org/abs/2011.14269v4
• Date: Tue, 2 Mar 2021 03:57:31 GMT
• Title: Generalization and Memorization: The Bias Potential Model
• Authors: Hongkang Yang and Weinan E
• Abstract summary: generative models and density estimators behave quite differently from models for learning functions. For the bias potential model, we show that dimension-independent generalization accuracy is achievable if early stopping is adopted. In the long term, the model either memorizes the samples or diverges.
• Score: 9.975163460952045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Models for learning probability distributions such as generative models and density estimators behave quite differently from models for learning functions. One example is found in the memorization phenomenon, namely the ultimate convergence to the empirical distribution, that occurs in generative adversarial networks (GANs). For this reason, the issue of generalization is more subtle than that for supervised learning. For the bias potential model, we show that dimension-independent generalization accuracy is achievable if early stopping is adopted, despite that in the long term, the model either memorizes the samples or diverges.

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