Evolving optimum camouflage with Generative Adversarial Networks
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AbstractWe describe a novel method to exploit Generative Adversarial Networks to simulate an evolutionary arms race between the camouflage of a synthetic prey and its predator. Patterns evolved using our methods are shown to provide progressively more effective concealment and outperform two recognised camouflage techniques. The method will be invaluable, particularly for biologists, for rapidly developing and testing optimal camouflage or signalling patterns in multiple environments.
2021 ◽
Vol 70
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pp. 1-17
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2020 ◽
Vol XLIII-B3-2020
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pp. 1219-1227
2020 ◽
Vol 29
(15)
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pp. 2050250
2020 ◽
Vol 34
(07)
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pp. 10853-10860