Authors:
Marius Schubert
1
;
Tobias Riedlinger
2
;
Karsten Kahl
1
and
Matthias Rottmann
1
Affiliations:
1
School of Mathematics and Natural Sciences, IZMD, University of Wuppertal, Germany
;
2
Institute of Mathematics, Technical University Berlin, Germany
Keyword(s):
Active Learning, Label Noise, Robustness, Label Error Detection, Object Detection.
Abstract:
Obtaining annotations for complex computer vision tasks such as object detection is an expensive and timeintense endeavor involving numerous human workers or expert opinions. Reducing the amount of annotations
required while maintaining algorithm performance is, therefore, desirable for machine learning practitioners
and has been successfully achieved by active learning. However, it is not merely the amount of annotations
which influences model performance but also the annotation quality. In practice, oracles that are queried
for new annotations frequently produce significant amounts of noise. Therefore, cleansing procedures are
oftentimes necessary to review and correct given labels. This process is subject to the same budget as the initial
annotation itself since it requires human workers or even domain experts. Here, we propose a composite active
learning framework including a label review module for deep object detection. We show that utilizing part of
the annotation budg
et to correct the noisy annotations partially in the active dataset leads to early improvements
in model performance, especially when coupled with uncertainty-based query strategies. The precision of the
label error proposals significantly influences the measured effect of the label review. In our experiments we
achieve improvements of up to 4.5mAP points by incorporating label reviews at equal annotation budget.
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