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DeepSetNet: Predicting Sets with Deep Neural Networks
[article]
2017
arXiv
pre-print
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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
A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction
2020
Applications in Plant Sciences
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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