Noise Detection

Noise detection in data, encompassing images, audio, and tabular datasets, aims to identify and mitigate the impact of erroneous or corrupted information on machine learning model performance. Current research emphasizes developing robust algorithms, including gradient-boosted decision trees and convolutional neural networks (CNNs), often integrated with autoencoders or ensemble methods, to effectively identify noisy data points. These advancements are crucial for improving the accuracy and reliability of machine learning models across diverse applications, from speech enhancement and industrial defect detection to mitigating the effects of noisy labels in training datasets. The ultimate goal is to enhance the quality and trustworthiness of data-driven insights.