Improving Active Learning by Avoiding Ambiguous Samples

Limberg, Christian; Wersing, Heiko; Ritter, Helge

doi:10.1007/978-3-030-01418-6_51

Cited by 4 publications

(2 citation statements)

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“…The user study showed that baseline methods have the advantage to faster respond at the start of training. When training samples can be ambiguous, we showed that the used DQBE [11] approach has a huge impact in boosting the speed by querying only meaningful samples. However, our study showed that after 100 seconds the fast increase in accuracy of the baseline methods saturates.…”

Section: Resultsmentioning

confidence: 99%

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Active Learning for Image Recognition Using a Visualization-Based User Interface

Limberg

Krieger

Wersing³

et al. 2019

Lecture Notes in Computer Science

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This paper introduces a novel approach for querying samples to be labeled in active learning for image recognition. The user is able to efficiently label images with a visualization for training a classifier. This visualization is achieved by using dimension reduction techniques to create a 2D feature embedding from high-dimensional features. This is made possible by a querying strategy specifically designed for the visualization, seeking optimized bounding-box views for subsequent labeling. The approach is implemented in a web-based prototype. It is compared in-depth to other active learning querying strategies within a user study we conducted with 31 participants on a challenging data set. While using our approach, the participants could train a more accurate classifier than with the other approaches. Additionally, we demonstrate that due to the visualization, the number of labeled samples increases and also the label quality improves.

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Section: Resultsmentioning

confidence: 99%

“…If they were not sure about the label of an image, they could click the Skip button. After skipping an image we use DBQE [11] to prevent the querying of similar ambiguous images again, to speed up training.…”

Section: Methodsmentioning

confidence: 99%