Randomly reweighting a dataset and then optimizing a set of support points to match the weighted data produces diverse, interpretable sample sets at low cost, according to visual results on MNIST and CelebA.
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Weighted Support Points from Random Measures: An Interpretable Alternative for Generative Modeling
Randomly reweighting a dataset and then optimizing a set of support points to match the weighted data produces diverse, interpretable sample sets at low cost, according to visual results on MNIST and CelebA.