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A seismic random noise suppression method based on self-supervised deep learning and transfer learning
Random noise suppression is an essential task in the seismic data processing. In recent years deep learning methods have achieved superior results in...
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Transfer learning-based self-learning intrusion detection system for in-vehicle networks
Controller area networks (CANs) are the de-facto standard for in-vehicle networks and enable real-time data communication between the electronic...
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Effects of a homework implementation method (MITCA) on self-regulation of learning
The MITCA method (Homework Implementation Method) was developed with the purpose of turning homework into an educational resource capable of...
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A self-supervised learning model based on variational autoencoder for limited-sample mammogram classification
AbstractDeep learning models have found extensive application in medical imaging analysis, particularly in mammography classification. However, these...
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A self-supervised deep learning method for data-efficient training in genomics
Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By...
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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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Self-Transfer Learning Network for Multicolor Fabric Defect Detection
This paper presented a self-transfer learning network (STLN) for multicolor fabric defect detection. Deep neural networks were adopted to detect...
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Text-video retrieval method based on enhanced self-attention and multi-task learning
The explosive growth of videos on the Internet makes it a great challenge to use texts to retrieve the videos we need. The general method of...
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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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A point cloud self-learning network based on contrastive learning for classification and segmentation
In the field of point cloud representation learning, many self-supervised learning methods aim to address the issue of conventional supervised...
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DisRot: boosting the generalization capability of few-shot learning via knowledge distillation and self-supervised learning
Few-shot learning (FSL) aims to adapt quickly to new categories with limited samples. Despite significant progress in utilizing meta-learning for...
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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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Modeling undergraduate students’ learning dynamics between self-regulated learning patterns and community of inquiry
In online STEM courses, self-regulated learning (SRL) serves a critical role in academic success because students are required to monitor and...
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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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The Relations Among Students’ Digital Accessibility, Digital Competence, Self-Efficacy for Self-Direction in Learning and Self-Rated Performance in Engineering Virtual Laboratories
This study investigated the association between students’ perceptions of digital accessibility and competence, self-efficacy for self-directed...
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Contrastive self-supervised learning for diabetic retinopathy early detection
AbstractDiabetic Retinopathy (DR) is the major cause of blindness, which seriously threatens the world’s vision health. Limited medical resources...
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Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection
Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the use of convolutional neural networks in computer-aided...
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Self-explanation prompts in video learning: an optimization study
The self-explanation strategy motivates learners to actively select and integrate information, thereby fostering meaningful learning. To generate...
