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Qualitative Image Selection with Active Learning

Allander, Linnea and Nordtorp, Torben (2023)
Department of Automatic Control
Abstract
In this work, an active learning pipeline is presented that allows for comparative tests between 3 different types of data scoring methods, uncertainty selection, diversity selection, and loss selection. Tests and parameter sweeps indicate that hyperparameters like reshuffling and early stopping are required to ensure fair comparisons. We then apply our pipeline to test some naive scoring methods on two Computer Vision problems, Image Detection, and Object Detection. We conclude that comparing these scoring methods against random gives minor evaluation loss improvements on our datasets in the right circumstances, and discuss other aspects of data quality along the way.
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author
Allander, Linnea and Nordtorp, Torben
supervisor
organization
year
type
H3 - Professional qualifications (4 Years - )
subject
report number
TFRT-6198
other publication id
0280-5316
language
English
id
9134631
date added to LUP
2023-08-18 15:04:13
date last changed
2023-09-06 13:56:50
@misc{9134631,
  abstract     = {{In this work, an active learning pipeline is presented that allows for comparative tests between 3 different types of data scoring methods, uncertainty selection, diversity selection, and loss selection. Tests and parameter sweeps indicate that hyperparameters like reshuffling and early stopping are required to ensure fair comparisons. We then apply our pipeline to test some naive scoring methods on two Computer Vision problems, Image Detection, and Object Detection. We conclude that comparing these scoring methods against random gives minor evaluation loss improvements on our datasets in the right circumstances, and discuss other aspects of data quality along the way.}},
  author       = {{Allander, Linnea and Nordtorp, Torben}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Qualitative Image Selection with Active Learning}},
  year         = {{2023}},
}