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- W2951409528 abstract "Developing products based on machine learning algorithms require relevant andaccurate datasets. In particular when it comes to supervised learning algorithmswhose performance is directly related to the quality and amount of training data.Within the field of computer vision, classification is a task that require training datain the form of annotated images. Annotating images is a manual task and I proposethat the annotation difficulty of an image should be interpreted as the likelihoodof someone else annotating an image differently. Knowing in advance which imagesare hard to annotate would facilitate the distribution of work between annotatorswith varying experience. In this thesis, it is shown that the uncertainty derived fromMonte Carlo dropout resembles the variance of a group of persons annotations of thesame image. This finding indicates that the level of agreement between persons canbe predicted, and thus enable for better distribution of work between annotators.Furthermore, the finding could also be used to order images during training byprioritizing harder images higher." @default.
- W2951409528 created "2019-06-27" @default.
- W2951409528 creator A5080627538 @default.
- W2951409528 date "2019-01-01" @default.
- W2951409528 modified "2023-09-27" @default.
- W2951409528 title "Predicting annotation difficulty using Monte Carlo dropout" @default.
- W2951409528 hasPublicationYear "2019" @default.
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