  Deep-learning methods offer unsurpassed recognition performance in a wide
range of domains, including fine-grained recognition tasks. However, in most
problem areas there are insufficient annotated training samples. Therefore, the
topic of transfer learning respectively domain adaptation is particularly
important. In this work, we investigate to what extent unsupervised domain
adaptation can be used for fine-grained recognition in a biodiversity context
to learn a real-world classifier based on idealized training data, e.g.
preserved butterflies and plants. Moreover, we investigate the influence of
different normalization layers, such as Group Normalization in combination with
Weight Standardization, on the classifier. We discovered that domain adaptation
works very well for fine-grained recognition and that the normalization methods
have a great influence on the results. Using domain adaptation and Transferable
Normalization, the accuracy of the classifier could be increased by up to 12.35
% compared to the baseline. Furthermore, the domain adaptation system is
combined with an active learning component to improve the results. We compare
different active learning strategies with each other. Surprisingly, we found
that more sophisticated strategies provide better results than the random
selection baseline for only one of the two datasets. In this case, the distance
and diversity strategy performed best. Finally, we present a problem analysis
of the datasets.
