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Reducing Risk and Uncertainty of Deep Neural Networks on Diagnosing COVID-19 Infection
In this work, we introduce uncertainty estimation to detect confusing cases for expert referral to address the unreliability of state-of-the-art (SOTA) DNNs on COVID-19 detection.
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention
The success of Deep Neural Networks (DNNs) highly depends on data quality.
Towards Robust Human Activity Recognition from RGB Video Stream with Limited Labeled Data
Due to lack of depth information, RGB video based activity recognition performs poorly compared to RGB-D video based solutions.
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