  The success of Deep Neural Networks (DNNs) highly depends on data quality.
Moreover, predictive uncertainty makes high performing DNNs risky 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 can effectively denoise training data as well as avoid predicting
uncertain test data points. Our proposed framework leverages underlying data
distribution to differentiate between noise and clean data samples without
requiring any modification to existing DNN architectures or loss functions.
Extensive experiments on multiple image classification datasets and multiple
CNN architectures demonstrate that our simple yet effective framework can
outperform the state-of-the-art techniques in denoising training data and
abstaining uncertain test data.
