Measuring global properties of neural generative model outputs via generating mathematical objects
This work addresses the challenge of evaluating generative models for complex mathematical objects, though it is incremental as it applies existing methods to a new domain.
The researchers trained deep generative models on complete datasets of reflexive polytopes to assess how well the models capture global properties of generated samples, showing that the models learn underlying geometric properties rather than merely memorizing data.
We train deep generative models on datasets of reflexive polytopes. This enables us to compare how well the models have picked up on various global properties of generated samples. Our datasets are complete in the sense that every single example, up to changes of coordinate, is included in the dataset. Using this property we also perform tests checking to what extent the models are merely memorizing the data. We also train models on the same dataset represented in two different ways, enabling us to measure which form is easiest to learn from. We use these experiments to show that deep generative models can learn to generate geometric objects with non-trivial global properties, and that the models learn some underlying properties of the objects rather than simply memorizing the data.