# A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention Source: [A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:35:00.393Z Authors: Krishanu Sarker, Xiulong Yang, Yang Li, Saeid Belkasim, Shihao Ji Published: 2020-09-25T04:18:08.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()