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Active and Incremental Learning with Weak Supervision

dc.contributor.authorBrust, Clemens-Alexander
dc.contributor.authorKäding, Christoph
dc.contributor.authorDenzler, Joachim
dc.date.accessioned2021-04-23T09:34:06Z
dc.date.available2021-04-23T09:34:06Z
dc.date.issued2020
dc.description.abstractLarge amounts of labeled training data are one of the main contributors to the great success that deep models have achieved in the past. Label acquisition for tasks other than benchmarks can pose a challenge due to requirements of both funding and expertise. By selecting unlabeled examples that are promising in terms of model improvement and only asking for respective labels, active learning can increase the efficiency of the labeling process in terms of time and cost. In this work, we describe combinations of an incremental learning scheme and methods of active learning. These allow for continuous exploration of newly observed unlabeled data. We describe selection criteria based on model uncertainty as well as expected model output change (EMOC). An object detection task is evaluated in a continuous exploration context on the PASCAL VOC dataset. We also validate a weakly supervised system based on active and incremental learning in a real-world biodiversity application where images from camera traps are analyzed. Labeling only 32 images by accepting or rejecting proposals generated by our method yields an increase in accuracy from 25.4 to 42.6%.de
dc.identifier.doi10.1007/s13218-020-00631-4
dc.identifier.pissn1610-1987
dc.identifier.urihttp://dx.doi.org/10.1007/s13218-020-00631-4
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/36285
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 34, No. 2
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectActive learning
dc.subjectIncremental learning
dc.subjectObject detection
dc.subjectWeak supervision
dc.subjectWildlife surveillance
dc.titleActive and Incremental Learning with Weak Supervisionde
dc.typeText/Journal Article
gi.citation.endPage180
gi.citation.startPage165

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