This paper presents the first review of noise models in classification covering both label and
attribute noise. Their study reveals the lack of a unified nomenclature in this field. In order to …

Deep-learning-based synthetic aperture radar automatic target recognition (SAR-ATR) plays
a significant role in the military and civilian fields. However, data limitation and large …

Because of the high cost of data acquisition in synthetic aperture radar (SAR) target
recognition, the application of synthetic (simulated) SAR data is becoming increasingly …

Deep neural networks (DNNs) appear to be a solution for the classification of polarimetric
synthetic aperture radar (PolSAR) data in that they outperform classical supervised …

In supervised machine learning, use of correct labels is extremely important to ensure high
accuracy. Unfortunately, most datasets contain corrupted labels. Machine learning models …

Convolutional neural network (CNN) still suffers from overfitting problems caused by limited
samples in SAR target classification. Few-shot learning (FSL) aims to learn a classifier to …

Despite achieving considerable success, the fault diagnosis methods will still be disturbed
by noisy labels, this causes the model's degradation and reduced diagnostic precision …

Recently, plenty of millimeter-wave image concealed object detection models have
achieved superior performance on benchmark datasets. The success of these models …

深度监督学习在合成孔径雷达自动目标识别任务中的成功依赖于大量标签样本. 但是,
在大规模数据集中经常存在错误(噪声) 标签, 很大程度降低网络训练效果 …

This paper aims to quantify the errors in the provided agricultural crop types, estimate the
possible error rate in the available dataset, and propose a correction strategy. This …