2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-1.9
Paper Title A UNIFIED DENSITY-DRIVEN FRAMEWORK FOR EFFECTIVE DATA DENOISING AND ROBUST ABSTENTION
Authors Krishanu Sarker, Xiulong Yang, Yang Li, Saeid Belkasim, Shihao Ji, Georgia State University, United States
SessionMLR-APPL-IP-1: Machine learning for image processing 1
LocationArea E
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The success of Deep Neural Networks (DNNs) highly depends on data quality. Moreover, predictive uncertainty reduces reliability of DNNs for real-world deployment. In this paper, we aim to address these two issues by proposing a unified filtering framework leveraging underlying data density, that effectively denoises training data as well as avoids predicting confusing samples. Our proposed framework differentiates noise from clean data samples without any modifying existing DNN architectures or loss functions. Extensive experiments on multiple benchmark datasets and recent COVIDx dataset demonstrate the effectiveness of our framework over state-of-the-art (SOTA) methods in denoising training data and abstaining uncertain test data.