Abstract
Recent work has shown generative adversarial networks (GANs) can generate highly realistic images, that are often indistinguishable (by humans) from real images. Most images so generated are not contained in the training dataset, suggesting potential for augmenting …
Original language | English (US) |
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Journal | International Conference on … |
State | Published - 2020 |
Externally published | Yes |