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| http://dx.doi.org/10.3389/fgene.2020.00791 | DOI Listing |
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Introduction: Active learning can significantly decrease the labeling cost of deep learning workflows by prioritizing the limited labeling budget to high-impact data points that have the highest positive impact on model accuracy. Active learning is especially useful for semantic segmentation tasks where we can selectively label only a few high-impact regions within these high-impact images. Most established regional active learning algorithms deploy a static-budget querying strategy where a fixed percentage of regions are queried in each image.
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Bivalve mollusks are globally distributed in marine and freshwater habitats. While exhibiting a relatively uniform bodyplan that is characterized by their eponymous bivalved shell that houses the soft-bodied animal, many lineages have acquired unique morphological, physiological, and molecular innovations that account for their high adaptability to the various properties of aquatic environments such as salinity, flow conditions, or substrate composition. This renders them ideal candidates for studies into the evolutionary trajectories that have resulted in their diversity, but also makes them important players for research concerned with climate change-induced warming and acidification of aquatic habitats.
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