The accurate segmentation of apple leaf disease spots is the key to identifying the classification of apple leaf diseases and disease severity. Therefore, a DeepLabV3+ semantic segmentation network model with an actors spatial pyramid pool module (AS...
The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The a...
Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this is...
The prediction of the ablation rate of silicone rubber-based composites is of great significance to accelerate the development of flexible thermal protection materials. Herein, a method which combines uniform design experimentation, active learning,...
The fault diagnosis of rolling bearings is faced with the problem of a lack of fault data. Currently, fault diagnosis based on traditional convolutional neural networks decreases the diagnosis rate. In this paper, the developed adaptive residual shri...
Health data are generally complex in type and small in sample size. Such domain-specific challenges make it difficult to capture information reliably and contribute further to the issue of generalization. To assist the analytics of healthcare dataset...
In recent years, generative adversarial networks (GANs) have been proposed to generate simulated images, and some works of literature have applied GAN to the analysis of numerical data in many fields, such as the prediction of building energy consump...
To address the problems of inadequate training and low precision in prediction models with small-sample-size and incomplete data, a novel SALGAN-CatBoost-SSAGA framework is proposed in this paper. We utilize the standard K-nearest neighbor algorithm...
Highlight removal is a critical and challenging problem. In view of the complex highlight phenomenon on the surface of smooth liquor bottles in natural scenes, the traditional highlight removal algorithms cannot semantically disambiguate between all-...
Dental panoramic X-ray imaging, due to its high cost-effectiveness and low radiation dose, has become a widely used diagnostic tool in dentistry. Accurate tooth segmentation is crucial for lesion analysis and treatment planning, helping dentists to q...
Software defect prediction (SDP) is an effective technique to lower software module testing costs. However, the imbalanced distribution almost exists in all SDP datasets and restricts the accuracy of defect prediction. In order to balance the data di...
Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system’s normal operation, security, and dependability. Traditional insulator detection methods, on the oth...
This paper proposes a weakly supervised cross-domain person re-identification (Re-ID) method based on small sample data. In order to reduce the cost of data collection and annotation, the model design focuses on extracting and abstracting the informa...
Most machine learning algorithms only have a good recognition rate on balanced datasets. However, in the field of malicious traffic identification, benign traffic on the network is far greater than malicious traffic, and the network traffic dataset i...
We present a novel, parameterised radar data augmentation (RADIO) technique to generate realistic radar samples from small datasets for the development of radar-related deep learning models. RADIO leverages the physical properties of radar signals, s...
Small-object detection is a challenging task in computer vision due to the limited training samples and low-quality images. Transfer learning, which transfers the knowledge learned from a large dataset to a small dataset, is a popular method for impr...
With sentiment prediction technology, businesses can quickly look at user reviews to find ways to improve their products and services. We present the BertBilstm Multiple Emotion Judgment (BBMEJ) model for small-sample emotion prediction tasks to solv...
Small tabular datasets can produce uncertain performance estimates because limited sample information affects both model fitting and evaluation. This study developed a procedure calibrated through simulation to estimate the mean absolute discrepancy...
As the most commonly used attack strategy by Botnets, the Domain Generation Algorithm (DGA) has strong invisibility and variability. Using deep learning models to detect different families of DGA domain names can improve the network defense ability a...
Geohazards such as landslides, which are often accompanied by surface cracks, have caused great harm to public safety and property. If these surface cracks could be identified in time, this would be of great significance for the monitoring and early...
Samples in large-scale datasets may be mislabeled for various reasons, and deep models are inclined to over-fit some noisy samples using conventional training procedures. The key solution is to alleviate the harm of these noisy labels. Many existing...
Determining the appropriate sample size is crucial in clinical studies due to the potential limitations of small sample sizes in detecting true effects. This work introduces the use of Wasserstein Generative Adversarial Networks (WGANs) to create vir...
Printed circuit board (PCB) defect detection plays a crucial role in PCB production, and the popular methods are based on deep learning and require large-scale datasets with high-level ground-truth labels, in which it is time-consuming and costly to...
Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independ...
Functional magnetic resonance imaging (fMRI) is a commonly used method of brain research. However, due to the complexity and particularity of the fMRI task, it is difficult to find enough subjects, resulting in a small and, often, imbalanced dataset....
A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure o...
In practical classification tasks, the sample distribution of the dataset is often unbalanced; for example, this is the case in a dataset that contains a massive quantity of samples with weak labels and for which concrete identification is unavailabl...
Deep learning is successful in providing adequate classification results in the field of traffic classification due to its ability to characterize features. However, malicious traffic captures insufficient data and identity tags, which makes it diffi...
In aerial image small-object detection, complex imaging perspectives, arbitrary object orientations, and long-tailed category distributions jointly exacerbate sample imbalance, which significantly degrades detection stability and leads to frequent mi...
Single-sample face recognition is a very challenging problem, where each person has only one labeled training sample. It is difficult to describe unknown facial variations. In this paper, we propose a shared generative adversarial network (SharedGAN)...
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...
In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive...
With the rapid development of artificial intelligence technology and unmanned surface vehicle (USV) technology, object detection and tracking have wide applications in marine monitoring and intelligent ships. However, object detection and tracking ta...
Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learnin...
Oil seals are vital components in industrial production, necessitating high-precision 3D reconstruction for automated geometric measurement and quality inspection. High-quality point cloud completion is integral to this process. However, existing met...
With their advantages in wildlife surveys and biodiversity monitoring, camera traps are widely used, and have been used to gather massive amounts of animal images and videos. The application of deep learning techniques has greatly promoted the analys...
Building polygons plays an important role in urban management. Although leveraging deep learning techniques for building polygon extraction offers advantages, the models heavily rely on a large number of training samples to achieve good generalizatio...
To address fault diagnosis under limited sample conditions, this paper proposes a small-sample diagnosis framework integrating multibody dynamic modeling and a GAN–CNN fusion strategy. First, a rigid–flexible coupled multibody dynamic mod...
For this project, we surveyed small mammal communities in protected forest habitats of the Sintra-Cascais Natural Park, Portugal, to establish a robust baseline for conservation planning. Our study provides a dataset and accompanying information to s...
The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples...
To address the demand for lightweight, high-precision, real-time, and low-computation detection of targets with limited samples—such as laboratory instruments in portable AR devices—this paper proposes a small dataset object detection alg...
The wide range, complex background, and small target size of aerial remote sensing images results in the low detection accuracy of remote sensing target detection algorithms. Traditional detection algorithms have low accuracy and slow speed, making i...
Vehicles in aerial images are generally with small sizes and unbalanced number of samples, which leads to the poor performances of the existing vehicle detection algorithms. Therefore, an oriented vehicle detection framework based on improved Faster...
Data integration has been proven to provide valuable information. The information extracted using data integration in the form of multiblock analysis can pinpoint both common and unique trends in the different blocks. When working with small multiblo...
The problem of deep learning network image classification when a large number of image samples are obtained in life and with only a small amount of knowledge annotation, is preliminarily solved in this paper. First, a support vector machine expert la...
To address the problem in predicting target prices for ship equipment orders where small sample sizes, high feature dimensions, and strong business constraints lead traditional models to overfit and have insufficient generalization ability, a method...
In the Big Data era, sampling remains a central theme. This paper investigates the characteristics of inverse sampling on two different datasets (real and simulated) to determine when big data become too small for inverse sampling to be used and to e...
Rolling bearings play a crucial role in industrial equipment, and their failure is highly likely to cause a series of serious consequences. Traditional deep learning-based bearing fault diagnosis algorithms rely on large amounts of training data; tra...
Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct mor...
Owing to the significant application potential of unmanned aerial vehicles (UAVs) and infrared imaging technologies, researchers from different fields have conducted numerous experiments on aerial infrared image processing. To continuously detect sma...
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