Böhlke,JuliaKorsch, DimitriBodesheim, PaulDenzler, Joachim2021-12-142021-12-142021978-3-88579-708-1https://dl.gi.de/handle/20.500.12116/37702Due to shrinking habitats, moth populations are declining rapidly. An automated moth population monitoring tool is needed to support conservationists in making informed decisions for counteracting this trend. A non-invasive tool would involve the automatic classification of images of moths, a fine-grained recognition problem. Currently, the lack of images annotated by experts is the main hindrance to such a classification model. To understand how to achieve acceptable predictive accuracies, we investigate the effect of differently sized datasets and data acquired from the Internet. We find the use of web data immensely beneficial and observe that few images from the evaluation domain are enough to mitigate the domain shift in web data. Our experiments show that counteracting the domain shift may yield a relative reduction of the error rate of over 60%. Lastly, the effect of label noise in web data and proposed filtering techniques are analyzed and evaluated.enWeb ImagesWebly Supervised LearningLabel NoiseNoise FilteringSpecies ClassificationFine-grained RecognitionMoth Scanner10.18420/informatik2021-0381617-5468