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We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. ... This paper addresses the task of set prediction using deep learning. ... neural network. ...arXiv:1611.08998v5 fatcat:xe4eo3yqfnhkfic36mbf3p544y
We adjusted the hyperparameters of a mask R-CNN (regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures ... We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. ... ACKNOWLEDGMENTS The authors thank the organizers of the iDigBio Phenology Deep Learning Workshop, which has allowed us to invest deeply in this research topic, and Jean-François Molino for his ideas during ...doi:10.1002/aps3.11368 pmid:32626610 pmcid:PMC7328656 fatcat:bwed35zxdzcbnoiehr7r5almai