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A novel feature and sample joint transfer learning method with feature selection in semi-supervised scenarios for identifying the sequence of some species with less known genetic data
When identifying the sequence of some species using fewer known gene training data (named target domain), the data of closely related species and...
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Small Sample MKFCNN-LSTM Transfer Learning Fault Diagnosis Method
Aiming at the problem that there are all kinds of noise interference in the planetary gearbox of wind turbine in the general experimental scene, the...
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A Model Transfer Learning Based Fault Diagnosis Method for Chemical Processes With Small Samples
Traditional fault diagnosis methods relies on sufficient fault samples, but it is unrealistic since the fault is a low possibility event in real...
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Self-supervised learning of hologram reconstruction using physics consistency
Existing applications of deep learning in computational imaging and microscopy mostly depend on supervised learning, requiring large-scale, diverse...
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Self-supervised learning for medical image analysis: a comprehensive review
Deep learning and advancements in computer vision offer significant potential for analyzing medical images resulting in better healthcare and...
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Self-supervised Learning: A Succinct Review
Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which...
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Multi-task learning with self-learning weight for image denoising
BackgroundImage denoising technology removes noise from the corrupted image by utilizing different features between image and noise. Convolutional...
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Joint spatio-temporal features constrained self-supervised electrocardiogram representation learning
The electrocardiogram (ECG) measurements with clinical diagnostic labels are intrinsically limited. We propose a generative learning based...
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Self-supervised learning advanced plant disease image classification with SimCLR
Supervised learning will be a bottleneck for develo** plant disease identification since it relies on learning from massive amounts of carefully...
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SEML: Self-Supervised Information-Enhanced Meta-learning for Few-Shot Text Classification
Training a deep-learning text classification model usually requires a large amount of labeled data, yet labeling data are usually labor-intensive and...
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Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms
The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms. Data-driven methods, including...
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Terrain traversability prediction through self-supervised learning and unsupervised domain adaptation on synthetic data
Terrain traversability estimation is a fundamental task for supporting robot navigation on uneven surfaces. Recent learning-based approaches for...
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Long-term student performance prediction using learning ability self-adaptive algorithm
Predicting student performance is crucial for both preventing failure and enabling personalized teaching-and-learning strategies. The digitalization...
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Self-similarity feature based few-shot learning via hierarchical relation network
Few-shot learning aims to recognize new visual concepts with a small number of labeled samples. The hierarchical structure based on inter-class...
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Self-attention transformer unit-based deep learning framework for skin lesions classification in smart healthcare
Rising mortality rates in recent years have elevated melanoma to the ranks of the world’s most lethal cancers. Dermoscopy images (DIs) have been used...
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Self-Supervised Representation Learning for Multivariate Time Series of Power Grid with Self-Distillation Augmentation
In recent years, a wealth of sensors have been configured in power grid scenarios to detect abnormal conditions so that the intelligent level of...
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SPP-EEGNET: An Input-Agnostic Self-supervised EEG Representation Model for Inter-dataset Transfer Learning
There is currently a scarcity of labeled Electroencephalography (EEG) recordings, and different datasets usually have incompatible setups (e.g.,...
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Classification of intelligent speech system and education method based on improved multi label transfer learning model
In recent years, improved multi label learning has been widely used in text classification, protein function prediction, image annotation and other...
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When to transfer: a dynamic domain adaptation method for effective knowledge transfer
Transfer learning has achieved a lot of success recently in saving training samples. However, most of the existing methods only focus on what and how...
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Automatic Traffic Light Detection for Self-Driving Cars Using Transfer Learning
Self-driving vehicles are being tested to make them more road-ready and safer for a real traffic environment. Automobile giants: like Tesla, Waymo,...