A novel framework using gated recurrent unit for fault diagnosis of rotary machinery with noisy labels
Sign Up to like & getrecommendations! Published in 2021 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/abd7a9
Abstract: Due to the harsh working environment, rotary machinery is susceptible to various faults, thus fault diagnosis to ensure safe operation is extremely important. Deep learning technology-based fault diagnosis is an effective method but may face… read more here.
Keywords: rotary machinery; noisy labels; fault diagnosis; diagnosis ... See more keywords
Limited Gradient Descent: Learning With Noisy Labels
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2954547
Abstract: Label noise may affect the generalization of classifiers, and the effective learning of main patterns from samples with noisy labels is an important challenge. Recent studies have shown that deep neural networks tend to prioritize… read more here.
Keywords: validation set; clean validation; validation; limited gradient ... See more keywords
From Street Photos to Fashion Trends: Leveraging User-Provided Noisy Labels for Fashion Understanding
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3069245
Abstract: There is increased interest in using street photos to understand fashion trends. Though street photos usually contain rich clothing information, there are several technical challenges to their analysis. First, street photos collected from social media… read more here.
Keywords: street; street photos; fashion trends; fashion ... See more keywords
Self-Augmentation Based on Noise-Robust Probabilistic Model for Noisy Labels
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3219810
Abstract: Learning deep neural networks from noisy labels is challenging, because high-capacity networks attempt to describe data even with noisy class labels. In this study, we propose a self-augmentation method without additional parameters, which handles noisy… read more here.
Keywords: noisy labels; small loss; probabilistic model; model ... See more keywords
DNN-Based PolSAR Image Classification on Noisy Labels
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3168799
Abstract: 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 classifiers under the condition of sufficient training samples. The design… read more here.
Keywords: noisy labels; dnn based; classification; training ... See more keywords
Self-Filtered Learning for Semantic Segmentation of Buildings in Remote Sensing Imagery With Noisy Labels
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3230625
Abstract: Not all building labels for training improve the performance of the deep learning model. Some labels can be falsely labeled or too ambiguous to represent their ground truths, resulting in poor performance of the model.… read more here.
Keywords: noisy labels; filtered learning; remote sensing; self filtered ... See more keywords
Robust Hyperspectral Image Domain Adaptation With Noisy Labels
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2018.2889800
Abstract: In hyperspectral image (HSI) classification, domain adaptation (DA) methods have been proved effective to address unsatisfactory classification results caused by the distribution difference between training (i.e., source domain) and testing (i.e., target domain) pixels. However,… read more here.
Keywords: domain adaptation; hyperspectral image; noisy labels; domain ... See more keywords
Learning From Noisy Labels for MIMO Detection With One-Bit ADCs
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2022.3230403
Abstract: This letter presents a data detection method for multiple-input multiple-output systems with one-bit analog-to-digital converters. The basic idea is to learn the likelihood function of the system from training samples. To this end, a training… read more here.
Keywords: noisy labels; one bit; data detection; learning noisy ... See more keywords
Attentive-Adaptive Network for Hyperspectral Images Classification With Noisy Labels
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2023.3254159
Abstract: With the development of deep neural networks, hyperspectral image (HSI) classification systems have achieved a significant improvement. These systems require numerous and accurately labeled hyperspectral data to be adequately trained. However, noisy labels are inherent… read more here.
Keywords: noisy labels; network; adaptive network; hsi classification ... See more keywords
Locomotion Mode Recognition Using Sensory Data With Noisy Labels: A Deep Learning Approach
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Mobile Computing"
DOI: 10.1109/tmc.2021.3135878
Abstract: Availability of various sensors in the smartphone makes it easier and convenient to collect the data of human locomotion activities. A recognition approach can utilize this sensory data for recognizing a locomotion mode of a… read more here.
Keywords: noisy labels; locomotion; recognition; sensory data ... See more keywords
Anti-Interference From Noisy Labels: Mean-Teacher-Assisted Confident Learning for Medical Image Segmentation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Medical Imaging"
DOI: 10.1109/tmi.2022.3176915
Abstract: Manually segmenting medical images is expertise-demanding, time-consuming and laborious. Acquiring massive high-quality labeled data from experts is often infeasible. Unfortunately, without sufficient high-quality pixel-level labels, the usual data-driven learning-based segmentation methods often struggle with deficient… read more here.
Keywords: noisy labels; quality; segmentation; labeled data ... See more keywords