Task weighting based on particle filter in deep multi-task learning with a view to uncertainty and performance

Emad Aghajanzadeh, Tahereh Bahraini, Amir Hossein Mehrizi, Hadi Sadoghi Yazdi

Abstract: Highlights•Deep multi-task networks have high uncertainty despite encouraging performance.•Multi-task learning system performance is sensitive to weighting strategy for tasks.•Paper proposes a novel weighting strategy to improve model uncertainty and accuracy.•This strategy enhances quality of weights by Bayesian estimator and particle filter.•Proposed tasks’ weights make model have low uncertainty and high performance.