Semi-supervised Classification Method Research Articles

A sea–land clutter classification framework for over-the-horizon radar based on weighted loss semi-supervised generative adversarial network

Deep convolutional neural network has made great achievements in sea–land clutter classification for over-the-horizon radar (OTHR). The premise is that a large number of labeled training samples must be provided for a sea–land clutter classifier. In practical engineering applications, it is relatively easy to obtain label-free sea–land clutter samples. However, the labeling process is extremely cumbersome and requires expertise in the field of OTHR. To solve this problem, we propose an improved generative adversarial network, namely weighted loss semi-supervised generative adversarial network (WL-SSGAN). Specifically, we propose a joint feature matching loss by weighting the middle layer features of the discriminator of semi-supervised generative adversarial network (SSGAN). Furthermore, we propose the weighted loss of WL-SSGAN by linearly weighting the standard adversarial loss of SSGAN and the joint feature matching loss. The classification performance of WL-SSGAN is evaluated on sea–land clutter datasets. The experimental results show that WL-SSGAN can improve the performance of the fully supervised classifier with only a small number of labeled samples by utilizing a large number of unlabeled sea–land clutter samples. Further, the proposed weighted loss is superior to both the adversarial loss and the feature matching loss. Additionally, we compare WL-SSGAN with conventional semi-supervised classification methods and demonstrate that WL-SSGAN achieves the highest classification accuracy.

Semi-supervised multiview fuzzy broad learning

Semi-supervised learning models often rely on restricted assumptions, and can easily suffer from covariate shift or noise. Few studies have investigated the use of fuzzy rule-based methods in the semi-supervised discipline. To improve model accuracy against covariate shift and to introduce fuzzy methods for interpretability, we first build a semi-supervised fuzzy broad learning model named SSFBLS, which employs a Mean-Teacher framework. Then, a trusted multiview semi-supervised classification method, termed TMSSC, is proposed by integrating the SSFBLS with a multiview fusion network to enhance the robustness of the model. Under the Mean-Teacher framework, SSFBLS involves the Takagi-Sugeno-Kang fuzzy model which can effectively deal with imprecision and uncertainty, and broad learning system which has strong learning ability and high computational efficiency. TMSSC utilizes a trusted mechanism to blend multiple views, so as to enhance its learning ability in semi-supervised scenarios. Experiments on the benchmark datasets demonstrate that the proposed methods have better anti-noise ability, competitive classification accuracy, as well as fast running speed.

BPSO-SLM: a binary particle swarm optimization-based self-labeled method for semi-supervised classification

BPSO-SLM: a binary particle swarm optimization-based self-labeled method for semi-supervised classification

FireMatch: A semi-supervised video fire detection network based on consistency and distribution alignment

Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process of data labeling is particularly costly and time-consuming, especially when dealing with videos. Considering the limited quantity of labeled video data, we propose a semi-supervised fire detection model called FireMatch, which is based on consistency regularization and adversarial distribution alignment. Specifically, we first combine consistency regularization with pseudo-label. For unlabeled data, we design video data augmentation to obtain corresponding weakly augmented and strongly augmented samples. The proposed model predicts weakly augmented samples and retains pseudo-label above a threshold, while training on strongly augmented samples to predict these pseudo-labels for learning more robust feature representations. Secondly, we generate video cross-set augmented samples by adversarial distribution alignment to expand the training data and alleviate the decline in classification performance caused by insufficient labeled data. Finally, we introduce a fairness loss to help the model produce diverse predictions for input samples, thereby addressing the issue of high confidence with the non-fire class in fire classification scenarios. The FireMatch achieved an accuracy of 76.92% and 91.80% on two real-world fire datasets, respectively. The experimental results demonstrate that the proposed method outperforms the current state-of-the-art semi-supervised classification methods.

FPGA-based remote target classification in hyperspectral imaging using multi-graph neural network

Hyperspectral imagery (HSI) is widely used in remote sensing for target classification; however, its accurate classification remains challenging due to the scarcity of labeled data. Graph Neural Networks (GNNs) have emerged as a popular method for semi-supervised classification, attracting significant interest in the context of HSI analysis. Nevertheless, conventional GNN-based approaches often rely on a single graph filter to extract HSI characteristics, failing to fully exploit the potential benefits of different graph filters. Additionally, oversmoothing issues plague classical GNNs, further affecting classification performance. To address these drawbacks, we propose a novel approach called Spectral and Autoregressive Moving Average Graph Filter for the Multi-Graph Neural Network (SAM-GNN). This approach leverages two distinct graph filters: one specialized in extracting the spectral characteristics of nodes and the other effectively suppressing graph distortion. Through extensive evaluations, we compare the performance of SAM-GNN with other state-of-the-art methods, employing metrics such as overall accuracy (OA), individual class accuracy (IA), and Kappa coefficient (KC). The results shows that the SAM-GNN provides an improvement in KC, IA, and OA of 6.71%, 5.7%, and 3.93% for the Pavia University dataset and 4.67%, 3.67%, and 3.49% for the Cuprite dataset respectively. Furthermore, we implement SAM-GNN on the Virtex-7 field-programmable gate array (FPGA), demonstrating that the method achieves highly accurate target localization results, bringing us closer to real-world applications in HSI classification.

From Segmentation to Classification: A Deep Learning Scheme for Sintered Surface Images Processing

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 learning scheme for mining the necessary information in sintered images processing to replace manual labor and realize intelligent inspection, consisting of segmentation and classification. Specifically, we first employ a DeepLabv3+ semantic segmentation algorithm to extract the effective material surface features. Unlike the original model, which includes a high number of computational parameters, we use SqueezeNet as the backbone to improve model efficiency. Based on the initial annotation of the processed images, the sintered surface dataset is constructed. Then, considering the scarcity of labeled data, a semi-supervised deep learning scheme for sintered surface classification is developed, which is based on pseudo-labels. Experiments show that the improved semantic segmentation model can effectively segment the sintered surface, achieving 98.01% segmentation accuracy with only a 5.71 MB size. In addition, the effectiveness of the adopted semi-supervised learning classification method based on pseudo-labels is validated in six state-of-the-art models. Among them, the ResNet-101 model has the best classification performance, with 94.73% accuracy for the semi-supervised strategy while only using 30% labeled data, which is an improvement of 1.66% compared with the fully supervised strategy.

Sample-weighted fused graph-based semi-supervised learning on multi-view data

Research in semi-supervised learning on graphs has attracted more and more attention in recent years, as learning on graphs is applied in more and more domains and labeling data is expensive and time-consuming. Some scenarios have inherent graph structures in their data, such as the relationships between people in social scenarios or the relationships between objects that are mutually referenced. However, there are also many data types without inherent graph structures, such as image data, and each image can be described with different features, which is a typical type of multi-view data. For image data and other non-graph data, there are significantly fewer deep learning approaches that target multi-view graph-based semi-supervised learning. This paper attempts to fill this gap. Based on the Graph Convolutional Network (GCN) architecture, we propose a Sample-weighted Fused Graph-based Semi-supervised Classification (WFGSC) method for multi-view data in this paper. It (i) constructs a semi-supervised graph in each view using a flexible model for joint graph and label estimation, (ii) obtains an additional graph based on the representation of nodes provided by the joint estimator, and then obtains a fused graph between all views, (iii) gives higher weights to hard-to-classify samples, (iv) proposes a loss function to train the GCN on the fused features and the consensus graph that integrates graph auto-encoder loss and label smoothing over the consensus graph. The results of our experiments on six multi-view datasets show that our WFGSC performs well on both fused graph construction and semi-supervised classification, and generally outperforms traditional GCNs and other multi-view semi-supervised multi-view classification methods.11Source code: https://github.com/BiJingjun/WFGSC.

Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

Time-sync comment (TSC) has emerged as a new type of textual comment for real-time user interactions on online video platforms. The sentiment classification of TSCs provides considerable potential for platforms to optimize operation strategies but inevitably faces great challenges due to the TSCs’ often uninformative and informal text. Considering the contextual dependency among TSCs posted within the same video clip, this study posits that contextual TSCs may benefit the sentiment classification of a target TSC. To address the challenges of leveraging contextual TSCs, such as their semantic representation and fusion, we propose a semi-supervised hierarchical deep learning method for the sentiment classification of TSCs. We design a hierarchical architecture to capture the semantics of TSCs at the word, comment, and context levels. Considering the varying importance of words and comments, we also design attention mechanisms to focus on important sentiment information and fuse semantic representations. Empirical evaluation shows that the proposed method outperforms benchmarked sentiment classification methods. This study advances our knowledge of contextual information indicative of TSC sentiment, and contributes to improving the service operation of online video platforms.

Customization of latent space in semi-supervised Variational AutoEncoder

We propose a novel semi-supervised learning method of Variational AutoEncoder (VAE), which yields a customized latent space through our EXplainable encoder Network (EXoN). The customization involves a manual design of the interpolation and structural constraint, such as proximity, which enhances the interpretability of the latent space. To improve the classification performance, we introduce a new semi-supervised classification method called SCI (Soft-label Consistency Interpolation). Combining the classification loss and the Kullback–Leibler divergence is crucial in constructing an explainable latent space. Additionally, the variability of the generated samples is determined by an active latent subspace, which effectively captures distinctive characteristics. We conduct experiments using the MNIST, SVHN, and CIFAR-10 datasets, and the results demonstrate that our approach yields an explainable latent space while significantly reducing the effort required to analyze representation patterns within the latent space.

A constrastive semi-supervised deep learning framework for land cover classification of satellite time series with limited labels

In this work, we present a new semi-supervised learning framework to cope with satellite image time series (SITS) classification in a data paucity scenario, considering extreme low levels of supervision. The proposed methodology, referred as S3ITS (Semi-Supervised Satellite Image Time Series classification method), is based on temporal convolutional neural networks and it takes advantage of both labelled and unlabelled information. S3ITS enforces the data to be projected in a discriminative manifold via contrastive learning, in order to produce a data representation where samples belonging to the same category are closer than the ones belonging to different ones. Pseudo-labelling is employed on unlabelled samples to take the most out of the available unlabelled information. Experiments on two study sites described by SITS of Sentinel-2 images highlight the quality of the proposed method with respect to common SITS-based classification methods and recent machine learning approaches especially tailored for the semi-supervised classification of multi-variate time series data.

GraphHop: An Enhanced Label Propagation Method for Node Classification.

A scalable semisupervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains all nodes' attributes and link connections but labels of only a subset of nodes. Graph convolutional networks (GCNs) have provided superior performance in node label classification over the traditional label propagation (LP) methods for this problem. Nevertheless, current GCN algorithms suffer from a considerable amount of labels for training because of high model complexity or cannot be easily generalized to large-scale graphs due to the expensive cost of loading the entire graph and node embeddings. Besides, nonlinearity makes the optimization process a mystery. To this end, an enhanced LP method, called GraphHop, is proposed to tackle these problems. GraphHop can be viewed as a smoothening LP algorithm, in which each propagation alternates between two steps: label aggregation and label update. In the label aggregation step, multihop neighbor embeddings are aggregated to the center node. In the label update step, new embeddings are learned and predicted for each node based on aggregated results from the previous step. The two-step iteration improves the graph signal smoothening capacity. Furthermore, to encode attributes, links, and labels on graphs effectively under one framework, we adopt a two-stage training process, i.e., the initialization stage and the iteration stage. Thus, the smooth attribute information extracted from the initialization stage is consistently imposed in the propagation process in the iteration stage. Experimental results show that GraphHop outperforms state-of-the-art graph learning methods on a wide range of tasks in graphs of various sizes (e.g., multilabel and multiclass classification on citation networks, social graphs, and commodity consumption graphs).

A semi-supervised medical image classification method based on combined pseudo-labeling and distance metric consistency

A semi-supervised medical image classification method based on combined pseudo-labeling and distance metric consistency

Joint learning of feature and topology for multi-view graph convolutional network

Graph convolutional network has been extensively employed in semi-supervised classification tasks. Although some studies have attempted to leverage graph convolutional networks to explore multi-view data, they mostly consider the fusion of feature and topology individually, leading to the underutilization of the consistency and complementarity of multi-view data. In this paper, we propose an end-to-end joint fusion framework that aims to simultaneously conduct a consistent feature integration and an adaptive topology adjustment. Specifically, to capture the feature consistency, we construct a deep matrix decomposition module, which maps data from different views onto a feature space obtaining a consistent feature representation. Moreover, we design a more flexible graph convolution that allows to adaptively learn a more robust topology. A dynamic topology can greatly reduce the influence of unreliable information, which acquires a more adaptive representation. As a result, our method jointly designs an effective feature fusion module and a topology adjustment module, and lets these two modules mutually enhance each other. It takes full advantage of the consistency and complementarity to better capture the more intrinsic information. The experimental results indicate that our method surpasses state-of-the-art semi-supervised classification methods.

A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework

Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance, and the models are more sensitive to error patches in the training samples. The training samples obtained in semi-supervised classification methods need less reliable weakly labelled samples, but current semi-supervised classification research puts the generated weak samples directly into the model for applications, with less consideration of the impact of the accuracy and quality improvement of the weak samples on the subsequent model classification. Therefore, to address the problem of generating and optimising the quality of weak samples from training data in deep learning, this paper proposes a semi-supervised building classification framework. Firstly, based on the test results of the remote sensing image segmentation model and the unsupervised classification results of LiDAR point cloud data, this paper quickly generates weak image samples of buildings. Secondly, in order to improve the quality of the spots of the weak samples, an iterative optimisation strategy of the weak samples is proposed to compare and analyse the weak samples with the real samples and extract the accurate samples from the weak samples. Finally, the real samples, the weak samples, and the optimised weak samples are input into the semantic segmentation model of buildings for accuracy evaluation and analysis. The effectiveness of this paper’s approach was experimentally verified on two different building datasets, and the optimised weak samples improved by 1.9% and 0.6%, respectively, in the test accuracy mIoU compared to the initial weak samples. The results demonstrate that the semi-supervised classification framework proposed in this paper can be used to alleviate the model’s demand for a large number of real-labelled samples while improving the ability to utilise weak samples, and it can be used as an alternative to fully supervised classification methods in deep learning model applications that require a large number of training samples.

ReliaMatch: Semi-Supervised Classification with Reliable Match

Deep learning has been widely used in various tasks such as computer vision, natural language processing, predictive analysis, and recommendation systems in the past decade. However, practical scenarios often lack labeled data, posing challenges for traditional supervised methods. Semi-supervised classification methods address this by leveraging both labeled and unlabeled data to enhance model performance, but they face challenges in effectively utilizing unlabeled data and distinguishing reliable information from unreliable sources. This paper introduced ReliaMatch, a semi-supervised classification method that addresses these challenges by using a confidence threshold. It incorporates a curriculum learning stage, feature filtering, and pseudo-label filtering to improve classification accuracy and reliability. The feature filtering module eliminates ambiguous semantic features by comparing labeled and unlabeled data in the feature space. The pseudo-label filtering module removes unreliable pseudo-labels with low confidence, enhancing algorithm reliability. ReliaMatch employs a curriculum learning training mode, gradually increasing training dataset difficulty by combining selected samples and pseudo-labels with labeled data. This supervised approach enhances classification performance. Experimental results show that ReliaMatch effectively overcomes challenges associated with the underutilization of unlabeled data and the introduction of error information, outperforming the pseudo-label strategy in semi-supervised classification.

Perturbed peer model with joint confidence for semi-supervised object-based classification in urban area

ABSTRACT The application of deep learning (DL) improves the accuracy of object-based classification in urban area, but the huge numbers of labelled samples required for training DL models are difficult to obtain. To address that, we propose a novel semi-supervised object-based classification method that combines pseudo-labelling method and consistency regularization method, named as perturbed peer model (PPM). To evaluate the quality of unlabelled object samples, a new object-level joint confidence is designed to assess the confidence of the samples’ prediction, which provides guidance for selecting unlabelled object samples in semi-supervised object-based classification. Instead of only using the high-confidence samples as usual, both the high-confidence samples with discriminative power and the low-confidence samples with a large range of structural features are exploited using pseudo-labelling method and consistency regularization method, respectively, facilitating the fusion of the two methods in the proposed PPM. In addition, two types of perturbations, dropout and difference augmentation, are integrated into the model to drive the consistency regularization method as well as to enhance the difference to facilitate the pseudo-labelling method. Experimental results show that the classification accuracy of PPM is better than the widely-used self-training, co-training, noisy-student, unsupervised data augmentation for consistency training, and FreeMatch methods in urban area.

Graph neural network-based remote target classification in hyperspectral imaging

ABSTRACT Hyperspectral Imagery (HSI) onboard target classification is a frequently used technique in remote sensing. Due to the small volume of labelled data, the classification of HSI has been a challenging subject in recent study. The graph neural network (GNN) has become a popular method for semi-supervised classification, and its use with hyperspectral images has gained a lot of interest. A graph filter or single graph neural network has been typically employed in the earlier GNN-based approaches, and it is used to obtain HSI characteristics. It has not fully utilized the benefits of different graph filters or graph neural networks. The issue of oversmoothing also persists with the classical GNNs. We propose a spectral filter and an autoregressive moving average filter for the multi-graph neural network; (SAF-GNN) to address such drawbacks as the one mentioned above. The first is excellent at extracting the nodes’ spectral characteristics, and the second is effective at suppressing graph distortion. With the help of performance measures like overall accuracy (OA), individual class accuracy (ICA), and Kappa coefficient (KC), the performance of the proposed method was compared to that of other state-of-the-art methods. The result shows that the SAF-GNN provides an improvement in OA, ICA and KC of 2.21%, 7.93% and 0.025 for the cuprite dataset and 4.84%, 11.78% and 0.025 for the Pavia University dataset, respectively. Comparison results show that the proposed method is suitable for remote target classification problem.

WETLAND CHANGE DETECTION USING SENTINEL-2 IN THE PART OF LATVIA

In the article, the possible impact of changes on wetland were analysed by the semi-supervised classification method of statistical analysis. The Sentinel-2 raw data between two different seasons are combined together. The data preparation is shortly described in the article. Data is clustered with unsupervised method. The article describes a supervised method – how data credibility and classification can be estimated if its reference is poor quality.

A semi-supervised short text sentiment classification method based on improved Bert model from unlabelled data

Short text information has considerable commercial value and immeasurable social value. Natural language processing and short text sentiment analysis technology can organize and analyze short text information on the Internet. Natural language processing tasks such as sentiment classification have achieved satisfactory performance under a supervised learning framework. However, traditional supervised learning relies on large-scale and high-quality manual labels and obtaining high-quality label data costs a lot. Therefore, the strong dependence on label data hinders the application of the deep learning model to a large extent, which is the bottleneck of supervised learning. At the same time, short text datasets such as product reviews have an imbalance in the distribution of data samples. To solve the above problems, this paper proposes a method to predict label data according to semi-supervised learning mode and implements the MixMatchNL data enhancement method. Meanwhile, the Bert pre-training model is updated. The cross-entropy loss function in the model is improved to the Focal Loss function to alleviate the data imbalance in short text datasets. Experimental results based on public datasets indicate the proposed model has improved the accuracy of short text sentiment recognition compared with the previous update and other state-of-the-art models.

Semi-Supervised Learning for Medical Image Classification Based on Anti-Curriculum Learning

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, especially annotating medical images, which requires professional knowledge. Therefore, semi-supervised learning has shown its potential for medical image classification. In this paper, we propose a novel pseudo-labelling semi-supervised learning method for medical image classification. Firstly, we utilize the anti-curriculum strategy for model training to prevent the model from producing predictions with a high value from the samples which are similar with existed labeled data. Secondly, to produce more stable and accurate pseudo labels for unlabeled data, we generate the pseudo labels with ensemble predictions provided by the model with samples augmented by different augmentations. In addition, we refine the generated pseudo labels using the prediction of the model at the current epoch in order to make the model learn from itself and improve the model performance. Comparative experiments on the Chest X-ray14 dataset for a multi-label classification task and the ISIC 2018 dataset for a multi-class classification task are performed, and the experimental results show the effectiveness of our method.