Web: http://arxiv.org/abs/2201.08770

Jan. 24, 2022, 2:10 a.m. | Kaitlin Gili, Marta Mauri, Alejandro Perdomo-Ortiz

cs.LG updates on arXiv.org arxiv.org

Defining and accurately measuring generalization in generative models remains
an ongoing challenge and a topic of active research within the machine learning
community. This is in contrast to discriminative models, where there is a clear
definition of generalization, i.e., the model's classification accuracy when
faced with unseen data. In this work, we construct a simple and unambiguous
approach to evaluate the generalization capabilities of generative models.
Using the sample-based generalization metrics proposed here, any generative
model, from state-of-the-art classical generative …

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