张量(固有定义)
降噪
秩(图论)
计算机科学
图像去噪
人工智能
模式识别(心理学)
数学
纯数学
组合数学
作者
R. Harikumar,Susan E. Minkoff,Yifei Lou
标识
DOI:10.1109/ssiai59505.2024.10508687
摘要
Neural network training data is often corrupted by equipment malfunction or noise leading to red blurry and incomplete data. This paper proposes a combination of a reconstruction technique and a neural network to deal with data corruption in a machine vision task. Specifically, we consider minimizing the tensor nuclear norm for low-rank data completion and denoising and demonstrate the method's effectiveness using a convolutional neural network (CNN) for image classification. We conduct classification experiments on 3 datasets, showing consistently that training on reconstructed images achieves improved accuracy ranging from 7-25% over training using corrupted data.
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