Semi‐supervised multiple empirical kernel learning with pseudo empirical loss and similarity regularization
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22690
Abstract: Multiple empirical kernel learning (MEKL) is a scalable and efficient supervised algorithm based on labeled samples. However, there is still a huge amount of unlabeled samples in the real‐world application, which are not applicable for… read more here.
Keywords: labeled samples; empirical kernel; unlabeled samples; kernel learning ... See more keywords
A survey: Deep learning for hyperspectral image classification with few labeled samples
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.03.035
Abstract: Abstract With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain… read more here.
Keywords: labeled samples; hsi classification; learning; deep learning ... See more keywords
A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning
Sign Up to like & getrecommendations! Published in 2021 at "Mechanical Systems and Signal Processing"
DOI: 10.1016/j.ymssp.2020.107043
Abstract: Abstract Limited condition monitoring data are recorded with label information in practice, which make the fault identification task more challenging. A semi-supervised learning (SSL) approach can be employed to increase the identification performance of the… read more here.
Keywords: labeled samples; limited labeled; fault; fault diagnosis ... See more keywords
Malware Traffic Classification Using Domain Adaptation and Ladder Network for Secure Industrial Internet of Things
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3131981
Abstract: Malware traffic classification (MTC) is a key technology for anomaly and intrusion detection in secure Industrial Internet of Things (IIoT). Traditional MTC methods based on port, payload, and statistic depend on the manual-designed features, which… read more here.
Keywords: network; classification; traffic; internet things ... See more keywords
Local and Nonlocal Context-Aware Elastic Net Representation-Based Classification for Hyperspectral Images
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2017.2666118
Abstract: By representing a query sample as a linear combination of all labeled samples and then classifying it by evaluating which class leads to the minimal representation error, the representation-based classification methods have been successfully used… read more here.
Keywords: labeled samples; classification; context; based classification ... See more keywords
CC-SSL: A Self-Supervised Learning Framework for Crop Classification With Few Labeled Samples
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.2022.3211994
Abstract: Labeled samples with real crop types are important for crop classification, but the acquisition of large batches of labeled samples will consume many resources, so it is necessary to study crop classification based on few… read more here.
Keywords: crop; crop classification; supervised learning; framework ... See more keywords
Semisupervised Deep Convolutional Neural Networks Using Pseudo Labels for PolSAR Image Classification
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3036387
Abstract: Deep-learning-based methods have obtained satisfying results in polarimetric synthetic aperture radar (PolSAR) image classification. However, these methods require large numbers of labeled samples, which are usually time-consuming and high-priced for PolSAR images. To address this… read more here.
Keywords: pseudo labels; labeled samples; classification; polsar image ... See more keywords
Hyperspectral Image Classification With Contrastive Self-Supervised Learning Under Limited Labeled Samples
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3159549
Abstract: Hyperspectral image (HSI) classification is an active research topic in remote sensing. Supervised learning-based methods have been widely used in HSI classification tasks due to their powerful feature extraction capabilities for cases of sufficiently labeled… read more here.
Keywords: classification; supervised learning; self supervised; contrastive self ... See more keywords
Learning From Reliable Unlabeled Samples for Semi-Supervised SAR ATR
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3197892
Abstract: Synthetic aperture radar automatic target recognition (SAR ATR) has been suffering from the insufficient labeled samples as the annotation of SAR data is time-consuming. Thus, adding unlabeled samples into training has attracted the attention of… read more here.
Keywords: supervised sar; semi supervised; unlabeled samples; sar atr ... See more keywords
Spectral–Spatial Hyperspectral Image Classification Based on Multiple Views and Multigraphs Fusion With Few Labeled Samples
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3202817
Abstract: The issue of limited labeled samples is still grave in hyperspectral image classification. Semisupervised learning (SSL) utilizing both labeled and unlabeled samples promotes a solution to this issue. However, it has been found that the… read more here.
Keywords: fusion; multiple views; image classification; hyperspectral image ... See more keywords
Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2022.3173356
Abstract: Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance… read more here.
Keywords: loss; attribute recognition; drop loss; noisy labeled ... See more keywords