DenoGrad: Deep Gradient Denoising Framework for Enhancing the Performance of Interpretable AI Models

📝 Original Info

• Title: DenoGrad: Deep Gradient Denoising Framework for Enhancing the Performance of Interpretable AI Models
• ArXiv ID: 2511.10161
• Date: 2025-11-13
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (예: 홍길동, 김민수, 박서연 등) **

📝 Abstract

The performance of Machine Learning (ML) models, particularly those operating within the Interpretable Artificial Intelligence (Interpretable AI) framework, is significantly affected by the presence of noise in both training and production data. Denoising has therefore become a critical preprocessing step, typically categorized into instance removal and instance correction techniques. However, existing correction approaches often degrade performance or oversimplify the problem by altering the original data distribution. This leads to unrealistic scenarios and biased models, which is particularly problematic in contexts where interpretable AI models are employed, as their interpretability depends on the fidelity of the underlying data patterns. In this paper, we argue that defining noise independently of the solution may be ineffective, as its nature can vary significantly across tasks and datasets. Using a task-specific high quality solution as a reference can provide a more precise and adaptable noise definition. To this end, we propose DenoGrad, a novel Gradient-based instance Denoiser framework that leverages gradients from an accurate Deep Learning (DL) model trained on the target data -- regardless of the specific task -- to detect and adjust noisy samples. Unlike conventional approaches, DenoGrad dynamically corrects noisy instances, preserving problem's data distribution, and improving AI models robustness. DenoGrad is validated on both tabular and time series datasets under various noise settings against the state-of-the-art. DenoGrad outperforms existing denoising strategies, enhancing the performance of interpretable IA models while standing out as the only high quality approach that preserves the original data distribution.

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