Learning complementary representations via attention-based ensemble learning for cough-based covid-19 recognition

HIGHLIGHTS

• who: Ren Zhao and collaborators from the Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany have published the article: Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition, in the Journal: (JOURNAL)
• what: The authors propose an attention-based ensemble learning approach to learn complementary representations from cough samples. For the first time the authors propose assembling multiple cough-based COVID-19 recognition models (i.e., a feed-forward DNN model, an image-based model, and an audio-based model) with feature-/decision-level . . .
  

   

  

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