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- W4288287726 abstract "We evaluate the distribution learning capabilities of generative adversarial networks by testing them on synthetic datasets. The datasets include common distributions of points in $R^n$ space and images containing polygons of various shapes and sizes. We find that by and large GANs fail to faithfully recreate point datasets which contain discontinous support or sharp bends with noise. Additionally, on image datasets, we find that GANs do not seem to learn to count the number of objects of the same kind in an image. We also highlight the apparent tension between generalization and learning in GANs." @default.
- W4288287726 created "2022-07-28" @default.
- W4288287726 creator A5001457347 @default.
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- W4288287726 date "2019-07-04" @default.
- W4288287726 modified "2023-09-30" @default.
- W4288287726 title "Evaluating the distribution learning capabilities of GANs" @default.
- W4288287726 doi "https://doi.org/10.48550/arxiv.1907.02662" @default.
- W4288287726 hasPublicationYear "2019" @default.
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