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Auflistung nach Schlagwort "Domain adaptation"

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  • Textdokument
    Domain Adaptation for CNN Based Iris Segmentation
    (BIOSIG 2017, 2017) Jalilian,Ehsaneddin; Uhl,Andreas; Kwitt,Roland
    Convolutional Neural Networks (CNNs) have shown great success in solving key artificial vision challenges such as image segmentation. Training these networks, however, normally requires plenty of labeled data, while data labeling is an expensive and time-consuming task, due to the significant human effort involved. In this paper we propose two pixel-level domain adaptation methods, introducing a training model for CNN based iris segmentation. Based on our experiments, the proposed methods can effectively transfer the domains of source databases to those of the targets, producing new adapted databases. The adapted databases then are used to train CNNs for segmentation of iris texture in the target databases, eliminating the need for the target labeled data. We also indicate that training a specific CNN for a new iris segmentation task, maintaining optimal segmentation scores, is possible using a very low number of training samples.
  • Zeitschriftenartikel
    Efficient Supervision for Robot Learning Via Imitation, Simulation, and Adaptation
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Wulfmeier, Markus
    Recent successes in machine learning have led to a shift in the design of autonomous systems, improving performance on existing tasks and rendering new applications possible. Data-focused approaches gain relevance across diverse, intricate applications when developing data collection and curation pipelines becomes more effective than manual behaviour design. The following work aims at increasing the efficiency of this pipeline in two principal ways: by utilising more powerful sources of informative data and by extracting additional information from existing data. In particular, we target three orthogonal fronts: imitation learning, domain adaptation, and transfer from simulation.