Svetlana Pavlitskaya (M.Sc.)
Wissenschaftliche Mitarbeiterin
Werdegang
Svetlana Pavlitskaya studierte von 2011 bis 2018 Informatik an der RWTH Aachen. Die Schwerpunkte ihres Studiums lagen in den Gebieten Deep Learning und Computer Vision. Ihre Masterarbeit verfasste Sie über die Integration von Deep-Learning Komponenten in autonome Fahrzeugarchitekturen.
Seit November 2018 ist Svetlana Pavlitskaya als wissenschaftliche Mitarbeiterin am Forschungszentrum Informatik (FZI) in der Abteilung Technisch Kognitive Systeme (TKS) angestellt.
Publikationen
Konferenzbeitrag (1)
- Using Mixture of Expert Models to Gain Insights Into Semantic SegmentationInfoDetails
Pavlitskaya, Svetlana and Hubschneider, Christian and Weber, Michael and Moritz, Ruby and Huger, Fabian and Schlicht, Peter and Zollner, Marius, 2020
Not only correct scene understanding, but also abilityto understand the decision making process of neural net-works is essential for safe autonomous driving. Currentwork mainly focuses on uncertainty measures, often basedon Monte Carlo dropout, to gain at least some insight intoa models confidence. We investigate a mixture of expertsarchitecture to achieve additional interpretability while re-taining comparable result quality.By being able to use both the overall model output aswell as retaining the possibility to take into account individ-ual expert outputs, the agreement or disagreement betweenthose individual outputs can be used to gain insights into thedecision process. Expert networks are trained by splittingthe input data into semantic subsets,e.g. corresponding todifferent driving scenarios, to become experts in those do-mains. An additional gating network that is also trained onthe same input data is consequently used to weight the out-put of individual experts. We evaluate this mixture of expertsetup on the A2D2 dataset and achieve similar results to abaseline FRRN network trained on all available data, whilegetting additional information.
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Kontakt
Telefon: +49 721 9654-374
E-Mail: pavlitsk@fzi.de- Using Mixture of Expert Models to Gain Insights Into Semantic SegmentationInfoDetails