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The success of Deep Neural Networks (DNNs) highly depends on data quality.Moreover, predictive uncertainty makes high performing DNNs risky forreal-world deployment. In this paper, we aim to address these two issues byproposing a unified filtering framework leveraging underlying data density,that can effectively denoise training data as well as avoid predictinguncertain test data points. Our proposed framework leverages underlying datadistribution to differentiate between noise and clean data samples withoutrequiring any modification to existing DNN architectures or loss functions.Extensive experiments on multiple image classification datasets and multipleCNN architectures demonstrate that our simple yet effective framework canoutperform the state-of-the-art techniques in denoising training data andabstaining uncertain test data.

A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention