Rego Drumond, Rafael
[Author]
;
Schmidt-Thieme, Lars
[Degree supervisor];
Landwehr, Niels
[Other];
Lindauer, Marius
[Other];
Stubbemann, Maximilian
[Other]
University thesis:
Dissertation, Universität Hildesheim, 2024
Footnote:
Description:
Few-shot learning has become an effective technique for helping parametric models improve performance on various tasks by leveraging knowledge from similar datasets. However, existing few-shot learning approaches are limited in their application to specific data types and tasks. This thesis addresses the challenges of few-shot learning in different domains: unstructured data, time series forecasting, and human motion prediction. Overall, this thesis contributes to the advancement of few-shot learning techniques in diverse domains, providing valuable insights and innovative solutions to enhance the performance of parametric models in scenarios with limited labeled data.