One of the major aspects affecting the performance of the classification algorithms is the amount of labeled data which is available during the training phase. It is widely accepted that the labeling procedure of vast amounts of data is both expensiv...
Machine learning-based indoor localization used to suffer from the collection, construction, and maintenance of labeled training databases for practical implementation. Semi-supervised learning methods have been developed as efficient indoor localiza...
In this work, we investigated two issues: (1) How the fusion of lidar and camera data can improve semantic segmentation performance compared with the individual sensor modalities in a supervised learning context; and (2) How fusion can also be levera...
Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifie...
Semi-supervised learning has attracted widespread attention due to its ability to utilize both labeled and unlabeled data, leading to significant progress in recent years. Conventional semi-supervised learning approaches often rely on a strategy that...
In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current...
Background: Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system with a propensity to inflict severe neurological disability. Accurate and early prediction of MS progression is extremely crucial for its management and...
Clustering algorithms play a crucial role in early warning cybersecurity systems. They allow for the detection of new attack patterns and anomalies and enhance system performance. This paper discusses the problem of clustering data collected by a dis...
Behind the rapid development of deep learning methods, massive data annotations are indispensable yet quite expensive. Many active learning (AL) and semi-supervised learning (SSL) methods have been proposed to address this problem in image classifica...
The colonoscopy is the foremost technique for detecting polyps, where accurate segmentation is crucial for effective diagnosis and surgical preparation. Nevertheless, contemporary deep learning-based methods for polyp segmentation face substantial hu...
Disease classification based on machine learning has become a crucial research topic in the fields of genetics and molecular biology. Generally, disease classification involves a supervised learning style; i.e., it requires a large number of labelled...
Changes in the jet stream not only affect the persistence of climate change and the frequency of extreme weather but are also closely related to climate change phenomena such as global warming. The manual way of drawing the jet stream axes in meteoro...
Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs fo...
With the rapid increase in photovoltaic energy production, there is a need for smart condition monitoring systems ensuring maximum throughput. Complex methods such as drone inspections are costly and labor intensive; hence, condition monitoring by ut...
For automatic tumor segmentation in magnetic resonance imaging (MRI), deep learning offers very powerful technical support with significant results. However, the success of supervised learning is strongly dependent on the quantity and accuracy of lab...
Graph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing...
Surgical Phase Recognition (SPR) enables real-time, context-aware assistance during surgery, but its use remains limited by the cost and effort of dense video annotation. This study presents a Semi-Supervised Deep Learning framework for SPR in endosc...
Twin extreme learning machine (TELM) is a phenomenon of symmetry that improves the performance of the traditional extreme learning machine classification algorithm (ELM). Although TELM has been widely researched and applied in the field of machine le...
Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, w...
Semi-supervised learning is a learning pattern that can utilize labeled data and unlabeled data to train deep neural networks. In semi-supervised learning methods, self-training-based methods do not depend on a data augmentation strategy and have bet...
Oil drilling is the core process of oil and natural gas resources exploitation. Well overflow is one of the biggest threats to safety drilling. Prediction of the overflow in advance can effectively avoid the occurrence of this kind of accident. Howev...
Due to the incomplete nature of cognitive testing data and human subjective biases, accurately diagnosing mental disease using functional magnetic resonance imaging (fMRI) data poses a challenging task. In the clinical diagnosis of mental disorders,...
Image matting methods based on deep learning have made tremendous success. However, the success of previous image matting methods typically relies on a massive amount of pixel-level labeled data, which are time-consuming and costly to obtain. This pa...
In order to solve the problems of the small capacity of structured data and uneven distribution among classes in machine learning tasks, a supervised generation method for structured data called WAGAN and a cyclic sampling method named SACS (Semi-sup...
In recent years, the involvement of synthetic strongly labeled data, weakly labeled data, and unlabeled data has drawn much research attention in semi-supervised acoustic event detection (SAED). The classic self-training method carries out prediction...
In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Th...
Graph pseudo-labeling is an effective semi-supervised learning (SSL) approach to improve graph neural networks (GNNs) by leveraging unlabeled data. However, its success heavily depends on the reliability of pseudo-labels, which can often result in co...
At present, machine sense of smell has shown its important role and advantages in many scenarios. The development of machine sense of smell is inseparable from the support of corresponding data and algorithms. However, the process of olfactory data c...
Learning novel classes with a few samples per class is a very challenging task in deep learning. To mitigate this issue, previous studies have utilized an additional dataset with extensively labeled samples to realize transfer learning. Alternatively...
In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled...
Although deep learning has achieved great success in image classification, large amounts of labelled data are needed to make full use of the advantages of deep learning. However, annotating a large number of images is expensive and time-consuming, es...
Semi-supervised metric learning intends to learn a distance function from the limited labeled data as well as a large amount of unlabeled data to better gauge the similarities of any two instances than using a general distance function. However, most...
The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, t...
The good performance of the port activities in terminals is mainly conditioned by the dynamic response of the moored ship system at a berth. An adequate definition of the highly multivariate processes involved in the response of a moored ship at a be...
The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological m...
In recent years, deep learning has witnessed astonishing success in the field of remote sensing in images. Generally, deep learning requires a large amount of labeled training data. Nevertheless, in remote sensing, sufficient labeled data are scarce...
Chatter is one of the most deleterious phenomena during the machining process, and leads to a low quality of workpiece surface, a noisy workplace, and decreases in tool and machine life. In order to overcome these limitations and improve the machinin...
Due to the imperfect assembly process, the unqualified assembly of a missing gasket or lead seal will affect the product’s performance and possibly cause safety accidents. Machine vision method based on deep learning has been widely used in quality i...
Unmanned aerial vehicles (UAVs) are an efficient tool for monitoring forest fire due to its advantages, e.g., cost-saving, lightweight, flexible, etc. Semantic segmentation can provide a model aircraft to rapidly and accurately determine the location...
Burn injuries are a common traumatic condition, and the early diagnosis of burn depth is crucial for reducing treatment costs and improving survival rates. In recent years, image-based deep learning techniques have been utilized to realize the automa...
The continual expansion in the number of images poses a great challenge for the annotation of the data. Therefore, improving the model performance for image classification with limited labeled data has become an important problem to solve. To address...
Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by adv...
The number of leaves in maize seedlings is an essential indicator of their growth rate and status. However, manual counting of seedlings is inefficient and limits the scope of the investigation. Deep learning has shown potential for quickly identifyi...
Few-shot learning aims to solve the difficulty in obtaining training samples, leading to high variance, high bias, and over-fitting. Recently, graph-based transductive few-shot learning approaches supplement the deficiency of label information via un...
As one of the entropy-based methods, the k-Star algorithm benefits from information theory in computing the distances between data instances during the classification task. k-Star is a machine learning method with a high classification performance an...
In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning...
The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnos...
During the training phase of the supervised learning, it is not feasible to collect all the datasets of labelled data in an outdoor environment for the localization problem. The semi-supervised transfer learning is consequently used to pre-train a sm...
Generally, the action recognition task requires a vast amount of labeled data, which represents a time-consuming human annotation effort. To mitigate the dependency on labeled data, this study proposes Semi-Supervised and Iterative Reinforcement Lear...
Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for au...
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