Applications of deep convolutional neural networks to digitized natural history collections

Applications of deep convolutional neural networks to digitized natural history collections

Natural history collections contain data that are critical for many scientific endeavors. Recent efforts in mass digitization are generating large datasets from these collections that can provide unprecedented insight. Here, we present examples of how deep convolutional neural networks can be applie...

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Bibliographic Details
Main Authors:	Eric Schuettpelz, Paul Frandsen, Rebecca Dikow, Abel Brown, Sylvia Orli, Melinda Peters, Adam Metallo, Vicki Funk, Laurence Dorr
Format:	Article
Language:	English
Published:	Pensoft Publishers 2017-11-01
Series:	Biodiversity Data Journal
Subjects:	convolutional neural networks deep learning m
Online Access:	https://bdj.pensoft.net/articles.php?id=21139

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