Supervised ECG-based sleep apnea detection typically depends on large and fully annotated datasets, yet the rarity and cost of labeling apneic events often lead to substantial annotation scarcity in practice. This study provides a controlled evaluati...
Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes. However, labeled data are often scarce in many real-world applications, which poses...
Leveraging mid-resolution satellite images such as Landsat 8 for accurate farmland segmentation and land change monitoring is crucial for agricultural management, yet is hindered by the scarcity of labelled data for the training of supervised deep le...
Driven by the unprecedented availability of data, machine learning has become a pervasive and transformative technology across industry and science. Its importance to marine science has been codified as one goal of the UN Ocean Decade. While increasi...
To successfully train a deep neural network, a large amount of human-labeled data is required. Unfortunately, in many areas, collecting and labeling data is a difficult and tedious task. Several ways have been developed to mitigate the problem associ...
In the health monitoring of electromechanical transmission systems, the collected state data typically consist of only a minimal amount of labeled data, with a vast majority remaining unlabeled. Consequently, deep learning-based diagnostic models enc...
The problem of text classification has been a mainstream research branch in natural language processing, and how to improve the effect of classification under the scarcity of labeled samples is one of the hot issues in this direction. The current mod...
Massive Open Online Courses (MOOCs) have expanded global access to education but continue to struggle with high attrition rates. This study presents a comparative analysis of self-labeled Semi-Supervised Learning (SSL) algorithms for predicting learn...
Speech Emotion Recognition (SER) plays a crucial role in applications involving human-machine interaction. However, the scarcity of suitable emotional speech datasets presents a major challenge for accurate SER systems. Deep Neural Network (DNN)-base...
Detecting defects, especially when they are small in the early manufacturing stages, is critical to achieving a high yield in industrial applications. While numerous modern deep learning models can improve detection performance, they become less effe...
Due to the scarcity of labeled samples, the practical engineering application of deep learning-based hydraulic pump fault diagnosis methods is extremely challenging. This study proposes a semi-supervised learning method based on data augmented consis...
Automatic modulation classification (AMC) plays a crucial role in wireless communication by identifying the modulation scheme of received signals, bridging signal reception and demodulation. Its main challenge lies in performing accurate signal proce...
Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully appli...
In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical i...
Autoimmune diseases constitute a broadly prevalent category of disorders. Conventional computer-aided diagnostic (CAD) techniques rely on large volumes of data paired with reliable annotations. However, the diverse symptomatology and diagnostic compl...
This paper presents a weakly supervised learning framework for real-world event identification in transmission networks using phasor measurement unit (PMU) data. The growing integration of renewable energy sources has introduced greater variability i...
Human action recognition (HAR) technology based on radar signals has garnered significant attention from both industry and academia due to its exceptional privacy-preserving capabilities, noncontact sensing characteristics, and insensitivity to light...
This paper addresses semi-supervised anomaly detection in settings where only a small subset of normal data can be labeled. Such conditions arise, for example, in industrial quality control of windshield wiper noise, where expert labeling is costly a...
Accurate estimation of the state of health (SOH) of lithium-ion batteries confronts two critical challenges: the extreme scarcity of labeled data in large-scale operational datasets and the mismatch between existing methods (relying on full charging&...
Deep learning-based SAR oil spill detection faces significant challenges due to limited labeled training data. To address this, we propose SinGAN-Labeler, an enhanced framework that generates high-quality synthetic SAR oil spill images and their labe...
Anomaly detection has gained increasing attention in recent years, but detecting anomalies in time series data remains challenging due to temporal dynamics, label scarcity, and data diversity in real-world applications. To address these challenges, w...
Multi-label text classification is a fundamental and important task in natural language processing, with widespread applications in specialized domains such as sentiment analysis, legal document classification, and medical coding. However, real-world...
Soft sensor technology has become an effective tool to enable real-time estimations of key quality variables in industrial rubber-mixing processes, which facilitates efficient monitoring and a control of rubber manufacturing. However, it remains a ch...
The use of deep learning methods in medical imaging has been able to deliver promising results; however, the success of such models highly relies on large, properly annotated datasets. The annotation of medical images is a laborious, expensive, and t...
Precisely identifying interior decoration styles holds substantial significance in directing interior decoration practices. Nevertheless, constructing accurate models for the automatic classification of interior decoration styles remains challenging...
Graph neural networks (GNNs) are effective for structured data analysis but face reduced learning accuracy due to noisy connections and the necessity for explicit graph structures and labels. This requirement constrains their usability in diverse gra...
Neural networks have been used in various computer vision applications, including noise removal. However, removing seismic noise via deep learning approaches faces a specific issue: the scarcity of labeled data. To address this difficulty, this work...
Bearing fault diagnosis under varying operating conditions faces challenges of domain shift and labeled data scarcity. This paper proposes a dual-stream hybrid-domain adaptation network (DS-HDA Net) that fuses CNN-extracted time-domain features with...
Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model trai...
The medical imaging domain frequently encounters the dual challenges of annotation scarcity and class imbalance. A critical issue lies in effectively extracting information from limited labeled data while mitigating the dominance of head classes. The...
Background/Objectives: Off-label drug use is prevalent in pediatric care, particularly in pediatric palliative care (PPC), due to the scarcity of pediatric-specific formulations and clinical trials. Differences in perception between healthcare profes...
Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST—a lightweight Rotationally Aware S...
In recent years, the ubiquity of social networks has transformed them into essential platforms for information dissemination. However, the unmoderated nature of social networks and the advent of advanced machine learning techniques, including generat...
Intelligent prediction, accurate diagnosis, and efficient repair of mechanical equipment faults are critical for ensuring production safety and enhancing efficiency in industrial processes. However, data scarcity and recognition efficiency remain sig...
The scarcity of high-quality labeled data poses a challenge to the application of deep learning (DL) in landslide identification from remote sensing (RS) images. Semi-supervised learning (SSL) has emerged as a promising approach to address the issue...
Effectively managing the quality of iron ore is critical to iron and steel metallurgy. Although quality inspection is crucial, the perspective of sintered surface identification remains largely unexplored. To bridge this gap, we propose a deep learni...
Emotion recognition is a vital task within Natural Language Processing (NLP) that involves automatically identifying emotions from text. As the need for specialized and nuanced emotion recognition models increases, the challenge of fine-grained emoti...
In semi-supervised learning (SSL) for medical image classification, model performance is often hindered by the scarcity of labeled data and the complexity of unlabeled data. This paper proposes an enhanced SSL approach to address these challenges by...
In aircraft feature detection, the difficulty of acquiring Synthetic Aperture Radar (SAR) images leads to the scarcity of some types of aircraft samples, and the high privacy makes the personal sample set have the characteristics of data silos. Exist...
Most of the existing deep learning algorithms are supervised learning and rely on a tremendous number of manually labeled samples. However, in most domains, due to the scarcity of samples or the excessive cost of labeling, it would be impracticable t...
Data-driven decision-making is the process of using data to inform your decision-making process and validate a course of action before committing to it. The quality of unlabeled data in real-world scenarios presents challenges for semi-supervised lea...
Food product authentication provides a means of monitoring and identifying products for consumer protection and regulatory compliance. There is a scarcity of analytical methods for confirming the identity of fruit pulp in products containing Soft Fru...
This study proposes a multi-source unsupervised domain adaptation framework for person re-identification (ReID), addressing cross-domain feature discrepancies and label scarcity in electric power field operations. Inspired by symmetry principles in f...
Deep learning methods have been widely studied for Polarimetric synthetic aperture radar (PolSAR) land cover classification. The scarcity of PolSAR labeled samples and the small receptive field of the model limit the performance of deep learning meth...
The limited availability of thermal infrared (TIR) training samples leads to suboptimal target representation by convolutional feature extraction networks, which adversely impacts the accuracy of TIR target tracking methods. To address this issue, we...
The increasing complexity and scale of cyber threats demand advanced, automated methodologies for extracting actionable cyber threat intelligence (CTI). The automated extraction of Tactics, Techniques, and Procedures (TTPs) from unstructured threat r...
The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised...
Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and na...
Knowledge graph completion can make knowledge graphs more complete, which is a meaningful research topic. However, the existing methods do not make full use of entity semantic information. Another challenge is that a deep model requires large-scale m...
Deep learning models and computer vision are commonly integrated for e-health self-help diagnosis. The abnormal colors and traits of feces can reveal the risks of cancer and digestive diseases. As such, this paper develops a self-help diagnostic syst...
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