Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model
Abstract
Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total phenotypic similarity across 2468 butterfly photographs, covering 38 subspecies from the polymorphic mimicry complex of Heliconius erato and Heliconius melpomene. Euclidean phenotypic distances, calculated using a deep convolutional triplet network, demonstrate significant convergence between interspecies...
Paper Details
Title
Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model
Published Date
Aug 1, 2019
Journal
Volume
5
Issue
8
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