Semi-supervised Active Learning Research Articles

Focus on informative graphs! Semi-supervised active learning for graph-level classification

Graph-level classification is a critical problem in social analysis and bioinformatics. Since annotated labels are typically costly, we intend to study this challenging task in semi-supervised scenarios with limited budgets. Inspired by the fact that active learning is capable of interactively querying an oracle to annotate a small number of informative examples in the unlabeled dataset, we develop a novel Semi-supervised active learning framework termed GraphSpa for graph-level classification. To make the most of labeling budgets, we propose an effective unlabeled data selection strategy that takes both local similarity and global semantic structure into account. Specifically, we first construct an adaptive queue with labeled samples and select informative samples that have a low degree of similarity to the queue using the Min-Max principle from the local view. Further, we introduce class prototypes and select samples with a large predictive loss discrepancy from the global view. To harness the full potential of unlabeled data, we develop a semi-supervised active learning framework on the basis of our fusion selection strategy coupled with graph contrastive learning during active learning. The effectiveness of our GraphSpa is validated against state-of-the-art methods through experimental results on diverse real-world benchmark datasets.

Interval Type-2 Fuzzy Neural Network Based on Active Semi-Supervised Learning for Non-Stationary Industrial Processes

Accurate models ensure the efficient and safe operations of industrial processes. However, modeling of industrial processes with non-stationary characteristic is challenging. In this study, an interval type-2 fuzzy neural network (IT2FNN) based on active semi-supervised learning (ASSL-IT2FNN) is proposed for such industrial processes. First, an IT2FNN with adaptive fuzzy membership function (FMF) and hierarchical learning method is utilized to achieve considerable modeling accuracy and efficiency. Second, to better tackle with the non-stationary characteristic, an unsupervised distribution detection method is proposed to identify the occurrence moments of concept drift actively from the perspective of probability and spatial projection. Then, by an active semi-supervised learning method, the concept drift samples with partial labels are used to build a subnetwork, guaranteeing the performance of the constructed IT2FNN models with incomplete data in non-stationary environments. Finally, the proposed ASSL-IT2FNN is verified by benchmark simulations and real industrial data, and the experimental results demonstrate its outperformance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Concept drift is an important indicator of a non-stationary environment. It manifests as changes in the distribution of samples over time, which may reduce the accuracy of models constructed based on historical samples. This study aims to alleviate the impact of concept drift on model performance in non-stationary industrial processes while reduce the cost of sample labeling. By using the adaptive FMF and hierarchical learning method, the designed IT2FNN can effectively deal with the inherent nonlinearity of complicated industrial processes. The samples representing concept drift are actively identified by the Kullback-Leibler (KL) divergence and maximum mean difference (MMD), enabling timely detections of concept drift moments in unsupervised situations. The active semi-supervised learning is used to adapt the constructed model to handle the concept drift, wherein a subnetwork is designed based on concept drift samples and similar historical samples.

Semi-supervised Active Learning for Video Action Detection

In this work, we focus on label efficient learning for video action detection. We develop a novel semi-supervised active learning approach which utilizes both labeled as well as un- labeled data along with informative sample selection for ac- tion detection. Video action detection requires spatio-temporal localization along with classification, which poses several challenges for both active learning (informative sample se- lection) as well as semi-supervised learning (pseudo label generation). First, we propose NoiseAug, a simple augmenta- tion strategy which effectively selects informative samples for video action detection. Next, we propose fft-attention, a novel technique based on high-pass filtering which enables effective utilization of pseudo label for SSL in video action detection by emphasizing on relevant activity region within a video. We evaluate the proposed approach on three different bench- mark datasets, UCF-101-24, JHMDB-21, and Youtube-VOS. First, we demonstrate its effectiveness on video action detec- tion where the proposed approach outperforms prior works in semi-supervised and weakly-supervised learning along with several baseline approaches in both UCF101-24 and JHMDB- 21. Next, we also show its effectiveness on Youtube-VOS for video object segmentation demonstrating its generalization capability for other dense prediction tasks in videos.

Online semi-supervised active learning ensemble classification for evolving imbalanced data streams

Concept drift is a core challenge in classification tasks of data streams. Although many drift adaptation methods have been presented, most of them assume that labels of all data are available, which is impractical in many real-world applications. Additionally, the absence of label makes the imbalance ratio of an imbalanced data stream difficultly being obtained in time, providing the inaccurate guidance for resampling and causing poor generalization. To tackle the joint challenges, an online semi-supervised active learning method is proposed to classifier imbalanced data streams with concept drift. A newly-arrived data is first added to the sliding window, and then assigned a pseudo label in terms of its nearest cluster. Meanwhile, semi-supervised clustering algorithm offers its predicted label. Based on the above two predictive labels, cluster-based query strategy provides the criteria for the evaluation and selection of representative instances. More especially, the uncertainty and importance of instances are defined to synthetically evaluate its representativeness. After obtaining true labels of typical ones, ensemble classifier is updated by all instances in current sliding window. Experimental results on 13 synthetic and real data streams indicate that the proposed method outperforms six comparative methods on both G-mean and Recall under various labeling budgets.

Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning

Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning

ASSL-HGAT: Active semi-supervised learning empowered heterogeneous graph attention network

Recently, heterogeneous graph attention network (HGAT) has been widely applied to various machine learning tasks and achieved remarkable results with sufficient labeled data. However, it is noteworthy that in many tasks, labeled data is scarce and the data labeling process is expensive. To that end, this paper presents a novel framework for learning from data with limited labels by organically integrating active learning (AL) and semi-supervised learning (SSL) into heterogeneous graph network. Our framework consists of three components: heterogeneous information network (HIN), HGAT and active semi-supervised learning strategy (ASSL). The adjustable HIN is first constructed by fusing multi-modal features. Then the HGAT is used to encode HIN and the double-layer attention mechanism can mitigate noises during information fusion. The ASSL is further designed to enrich the high-quality labeled training data for model train. Samples selected by uncertainty-aware AL and samples labeled by pseudo-label selection based SSL are finally mixed to iteratively train the ASSL-HGAT framework. Experimental results prove that ASSL-HGAT surpasses the compared state-of-the-art methods on five representative semi-supervised benchmark datasets under the same experimental settings.

Multichannel semi-supervised active learning for PolSAR image classification

Deep neural networks have recently been extensively utilized for Polarimetric synthetic aperture radar (PolSAR) image classification. However, this heavily relies on extensive labeled data which is both costly and labor-intensive. To lower the collection of labeling data and enhance the classification performance, a novel multichannel semi-supervised active learning (MSSAL) method is proposed for PolSAR image classification. First, a multichannel strategy-based committee model with cooperative representation classification is presented to explore more effective information in the limited training data. Second, a loss prediction (LP) module is designed to identify the most informative pixels, and an ensemble learning (EL) strategy is designed to select the pixels with the highest confidence. Then, the deep neural network is fine-tuned with the obtaining target pixels through LP and EL in each iteration. Finally, the trained deep model predicts labels for all unlabeled data, outputting the final classification results. The proposed method is evaluated on three real-world PolSAR datasets, demonstrating superior performance to other PolSAR image classification methods with limited labeled samples.

CASL: Capturing Activity Semantics through Location Information for Enhanced Activity Recognition.

Using portable tools to monitor and identify daily activities has increasingly become a focus of digital healthcare, especially for elderly care. One of the difficulties in this area is the excessive reliance on labeled activity data for corresponding recognition modeling. Labeled activity data is expensive to collect. To address this challenge, we propose an effective and robust semi-supervised active learning method, called CASL, which combines the mainstream semi-supervised learning method with a mechanism of expert collaboration. CASL takes a user's trajectory as the only input. In addition, CASL uses expert collaboration to judge the valuable samples of a model to further enhance its performance. CASL relies on very few semantic activities, outperforms all baseline activity recognition methods, and is close to the performance of supervised learning methods. On the adlnormal dataset with 200 semantic activities data, CASL achieved an accuracy of 89.07%, supervised learning has 91.77%. Our ablation study validated the components in our CASL using a query strategy and a data fusion approach.

An active semi-supervised transfer learning method for robot pose error prediction and compensation

Robots are widely employed in industrial settings owing to their efficiency, flexibility, and extensive operational ranges. However, their application in high-precision scenarios is limited owing to their low absolute accuracies. Existing methods suffer from high measurement costs, and limited applicability and accuracy. To address these issues, an active semi supervised transfer learning method (ASTL) is introduced. The pose error prediction problem was modelled as a transfer learning paradigm for the first time. It leverages the proposed multi-stage greedy sampling (MGS) for informed sample labelling combined with coarse calibration and semi supervised transfer learning (STL) to embed theoretical knowledge for globally accurate predictions. The proposed method is compared with other prediction and compensation approaches for four robotic motion tasks. It significantly reduces the time consumption by approximately 89.3% compared with direct measurements and achieves a maximum reduction of approximately 90% in robot pose errors. This substantial enhancement in pose accuracy promotes high-quality applications of robots in the industrial sector.

Cost-effective identification of the field maturity of tobacco leaves based on deep semi-supervised active learning and smartphone photograph

As a key technology for tobacco leaf harvesting, automatic identification of the field maturity of tobacco leaves is an important issue in the tobacco industry. For years, researchers have been exploring machine learning methods to perform the identification task, with traditional supervised learning (SL) schemes being studied in depth. However, the actual use of an SL algorithm is always costly and cumbersome, as it requires a large amount of labor and time to collect and label enough tobacco leaf samples. Therefore, a cost-effective algorithm solution is desired by the industry. To this end, we investigate the efficacy and efficiency of semi-supervised learning (SSL) scheme for this task, attempting to significantly reduce the labeling cost and provide a flexible way to control the cost. Considering the advantages of smartphones in terms of availability and economy, we use smartphone photography as an image acquisition tool, thereby building a tobacco leaf maturity dataset containing more than 7000 tobacco leaf images of three field maturity categories (unripe, ripe and overripe) of three stem positions (bottom, middle and top). According to the fact that the maturity of the unripe, ripe and overripe leaves increases sequentially, we propose a maturity structure constraint (MSC) to make the identification model converge to a confident space and be generalizable. Furthermore, we propose a sample selection method based on self-supervised representation learning, which can effectively select high-quality samples for the identification network. Finally, we design an SSL algorithm framework called maturity structure constraint based semi-supervised learning (MSC-SSL) for the identification task. Experimental results show that our SSL framework is capable to significantly reduce the sample usage and improve the identification accuracy. It achieves the accuracies of 91.92% (bottom position/with 33.16% training samples), 87.06% (middle position/with 27.57% training samples) and 87.64% (top position/with 28.02% training samples) when auto stopped, as compared to the SSL benchmarks of 87.37% and 90.40%, 83.53% and 85.10%, 81.65% and 84.64%, respectively. Furthermore, our method can match or exceed the accuracy of the SL scheme using only about 25% of the labeled samples required by the SL scheme.

Federated Active Semi-Supervised Learning With Communication Efficiency

Federated Active Semi-Supervised Learning With Communication Efficiency

Semi-supervised active learning hypothesis verification for improved geometric expression in three-dimensional object recognition

Semi-supervised active learning hypothesis verification for improved geometric expression in three-dimensional object recognition

Learning Methods and Predictive Modeling to Identify Failure by Human Factors in the Aviation Industry

This paper proposes a model capable of predicting fatal occurrences in aviation events such as accidents and incidents, using as inputs the human factors that contributed to each incident, together with information about the flight. This is important because aviation demands have increased over the years; while safety standards are very rigorous, managing risk and preventing failures due to human factors, thereby further increasing safety, requires models capable of predicting potential failures or risky situations. The database for this paper’s model was provided by the Aviation Safety Network (ASN). Correlations between leading causes of incident and the human element are proposed, using the Human Factors Analysis Classification System (HFACS). A classification model system is proposed, with the database preprocessed for the use of machine learning techniques. For modeling, two supervised learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN), and the semi-supervised Active Learning (AL) are considered. Their respective structures are optimized applying hyperparameter analysis to improve the model. The best predictive model, obtained with RF, was able to achieve an accuracy of 90%, macro F1 of 87%, and a recall of 86%, outperforming ANN models, with a lower ability to predict fatal accidents. These performances are expected to assist decision makers in planning actions to avoid human factors that may cause aviation incidents, and to direct efforts to the more important areas.

A novel anomaly detection approach based on ensemble semi-supervised active learning (ADESSA)

As an industrial infrastructure, the safety and reliability of the Cyber-Physical System requires the effective anomaly detection. However, the existing detection methods have bottleneck in the face of insufficient training datasets. This work proposed and a novel anomaly detection approach based on ensemble semi-supervised active learning, which can effectively detect anomalous traffic when there is few labeled samples and the dataset is unbalanced. Specifically, this work proposed balanced sampling strategy, which combines the margin sampling and the democratic co-learning techniques, to construct a balanced training set that consists of manually labeled high-information samples and automatically labeled high-confidence samples, to effectively train the detection model on a limited budget. We also found adding correctly labeled high-confidence samples into training set improves the performance of detection model when the training samples are few and the label budget is limited. This work achieves a good balance between the effectiveness of model training and the cost of sample querying when the traffic data in CPS is rare labeled and imbalanced. In addition, we designed five pairs of experiments with NSL-KDD and SWaT dataset, and the results demonstrate the capability and advancement of proposed approach.

Active Semi-Supervised Learning via Bayesian Experimental Design for Lung Cancer Classification Using Low Dose Computed Tomography Scans

We introduce an active, semisupervised algorithm that utilizes Bayesian experimental design to address the shortage of annotated images required to train and validate Artificial Intelligence (AI) models for lung cancer screening with computed tomography (CT) scans. Our approach incorporates active learning with semisupervised expectation maximization to emulate the human in the loop for additional ground truth labels to train, evaluate, and update the neural network models. Bayesian experimental design is used to intelligently identify which unlabeled samples need ground truth labels to enhance the model’s performance. We evaluate the proposed Active Semi-supervised Expectation Maximization for Computer aided diagnosis (CAD) tasks (ASEM-CAD) using three public CT scans datasets: the National Lung Screening Trial (NLST), the Lung Image Database Consortium (LIDC), and Kaggle Data Science Bowl 2017 for lung cancer classification using CT scans. ASEM-CAD can accurately classify suspicious lung nodules and lung cancer cases with an area under the curve (AUC) of 0.94 (Kaggle), 0.95 (NLST), and 0.88 (LIDC) with significantly fewer labeled images compared to a fully supervised model. This study addresses one of the significant challenges in early lung cancer screenings using low-dose computed tomography (LDCT) scans and is a valuable contribution towards the development and validation of deep learning algorithms for lung cancer screening and other diagnostic radiology examinations.

Semi-Supervised Active Learning for Anomaly Detection in Aviation

Anomaly detection in commercial aviation is an extremely challenging yet crucial task. Accurately detecting operationally significant anomalies can save civilian lives and/or result in significant savings in maintenance cost. The current practice uses manually tuned rule-based mechanisms to flag exceedances from predefined safety boundaries. However, this system cannot identify unknown risks and emerging vulnerabilities. Recently, innovative approaches based on machine learning have been used to automate anomaly detection. However, there are limits to their applicability in the field of aviation due to several challenges: 1) Properly reviewed data are scarce in aviation and, as a result, supervised learning cannot reach optimal performance. 2) Operationally significant anomalies do not coincide with statistically significant ones and, as a result, unsupervised learning fails to provide reliable and robust performance. In this paper, we propose a semi-supervised active learning framework for anomaly detection (SALAD), which detects operationally significant anomalies in flight operational quality assurance data. The developed framework works with vast amounts of unlabeled data as well as a small quantity of labeled data reviewed by subject matter experts to reliably identify safety anomalies in flight operations. Moreover, the model’s active learning strategy allows it to detect unknown anomalies that might emerge in the system. We validate the performance of the SALAD with a real-world case study of anomaly detection during the approach to landing of commercial aircraft. We specifically show that the proposed framework reaches reliable performance when only 1% of the data is labeled and can identify unknown anomalies effectively.

Automated Wound Image Segmentation: Transfer Learning from Human to Pet via Active Semi-Supervised Learning

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for chronic wounds. In this work, we introduced a novel pipeline for the segmentation of pet wound images. Starting from a model pre-trained on human-based wound images, we applied a combination of transfer learning (TL) and active semi-supervised learning (ASSL) to automatically label a large dataset. Additionally, we provided a guideline for future applications of TL+ASSL training strategy on image datasets. We compared the effectiveness of the proposed training strategy, monitoring the performance of an EfficientNet-b3 U-Net model against the lighter solution provided by a MobileNet-v2 U-Net model. We obtained 80% of correctly segmented images after five rounds of ASSL training. The EfficientNet-b3 U-Net model significantly outperformed the MobileNet-v2 one. We proved that the number of available samples is a key factor for the correct usage of ASSL training. The proposed approach is a viable solution to reduce the time required for the generation of a segmentation dataset.

Federated-Learning-Empowered Semi-Supervised Active Learning Framework for Intrusion Detection in ZSM

Exponential growth of novel radical applications and services in sixth-generation (6G) networks is expected to increase the complexity of managing existing network infrastructures. In this context, the zero touch network and service management (ZSM) paradigm, which leverages AI, SDN, and NFV techniques, is seen as a promising solution to automatically manage and orchestrate network resources. However, due to the closed-loop operation and automated end-to-end framework in a distributed 6G network, the ZSM architecture, along with its potential benefits, is exposed to various security threats. A recently proposed solution to address privacy concerns is federated learning (FL), whereby distributed training is performed, and the aggregated model parameters, instead of clients' raw data, are forwarded to the global server. However, most of the existing FL and semi-supervised learning (SSL) models for intrusion detection are based on the assumption that fully labeled data are always available at the server and client sides, which is not practical due to the high labeling costs and privacy constraints in the 6G network. In this article, we propose a novel FL-empowered semi-supervised active learning (FL-SSAL) security orchestration framework for the Label-at-Client scenario where, along with unlabeled samples, clients have a small portion of labeled data. The entropy-based active learning selects the most informative samples for data annotation and leverages the unlabeled data using a semi-supervised approach. The results of our experimental evaluations performed on the private, not independent and identically distributed (non-IID) dataset demonstrate that FL-SSAL achieves higher intrusion detection accuracy and has less communication overhead than baseline schemes with less labeled data.

Semi-Supervised Active Learning for Object Detection

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 classification tasks. However, these methods face a new challenge in object detection tasks, since object detection requires classification as well as localization information in the labeling process. Therefore, in this paper, an object detection framework combining active learning and semi-supervised learning is presented. Tailored for object detection tasks, the uncertainty of an unlabeled image is measured from two perspectives, namely classification stability and localization stability. The unlabeled images with low uncertainty are manually annotated as the AL part, and those with high uncertainty are pseudo-labeled with the detector’s prediction results as the SSL part. Furthermore, to better filter out the noisy pseudo-boxes brought by SSL, a novel pseudo-label mining strategy is proposed that includes a stability aggregation score (SAS) and dynamic adaptive threshold (DAT). The SAS aggregates the classification and localization stability scores to measure the quality of predicted boxes, while the DAT adaptively adjusts the thresholds for each category to alleviate the class imbalance problem. Extensive experimental results demonstrate that our proposed method significantly outperforms state-of-the-art AL and SSL methods.

Toward Label-Efficient Neural Network Training: Diversity-Based Sampling in Semi-Supervised Active Learning

Collecting large-labeled data is an expensive and challenging issue for training deep neural networks. To address this issue, active learning is recently studied where the active learner provides informative samples for labeling. Diversity-based sampling algorithms are commonly used for representation-based active learning. In this paper, a new diversity-based sampling is introduced for semi-supervised active learning. To select more informative data at the initial stage, we devise a diversity-based initial dataset selection method by using self-supervised representation. We further propose a new active learning query strategy, which exploits both consistency and diversity. Comparative experiments show that the proposed method can outperform other active learning approaches on two public datasets.