Systems and methods for labeling source data using confidence labels
- Creators
- Welinder, Peter
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Perona, Pietro
Abstract
Systems and methods for the annotation of source data using confidence labels in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining confidence labels for crowdsourced annotations includes obtaining a set of source data, obtaining a set of training data representative of the set of source data, determining the ground truth for each piece of training data, obtaining a set of training data annotations including a confidence label, measuring annotator accuracy data for at least one piece of training data, and automatically generating a set of confidence labels for the set of unlabeled data based on the measured annotator accuracy data and the set of annotator labels used.
Additional Information
Application filed: May 27, 2016. Patent granted: July 11, 2017.Attached Files
Published - US9704106B2.pdf
Files
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Additional details
- Eprint ID
- 87107
- Resolver ID
- CaltechAUTHORS:20180614-120834409
- Created
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2018-07-05Created from EPrint's datestamp field
- Updated
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2019-10-03Created from EPrint's last_modified field