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contrastive learning for efficient anomaly detection in electricity load data

SUSTAINABLE ENERGY GRIDS & NETWORKS 2025年 42卷

作者： Choubey, Mohit Chaurasiya, Rahul Kumar Yadav, J. S. Maulana Azad Natl Inst Technol Dept ECE Bhopal 462003 India

Identifying irregularities in electricity load data is essential for maintaining dependable and effective power systems. Traditional approaches necessitate a significant amount of labeled data in order to achieve high accuracy, resulting in increased costs, and limited scalability. This paper introduces a feature extraction model based on contrastive learning, which greatly enhances the accuracy of anomaly detection for electricity load data. The model generates both positive and negative pairs after utilizing original input data sequences. This enables to learn complex similarities and differences. Through the utilization of a contrastive loss function, the aim is to minimize disparities between positive pairs and maximize the distances between negative pairs, resulting in the extraction of essential feature representations. The results demonstrate significant improvements enhancements such as accuracy rose from 69.85 % to 95.65 %, precision improved from 61.2 % to 96 %, recall increased from 74.5 % to 93 %, and the F1-score saw an improvement from 67.3 % to 94.6 %. The ROC-AUC score rose from 0.7286 to 0.9532, indicating better differentiation between normal and anomalous data. A paired t-test confirmed these gains with p-values well below 0.05, further validating the model's effectiveness, while Cohen's d test validated the practical significance, indicating large effect sizes across all metrics. Furthermore, 95 % confidence intervals for the mean differences confirmed that the improvements are both statistically and practically meaningful. This approach not only improves detection accuracy but also reduces reliance on large labeled datasets, making it more scalable and cost-effective for real-world applications.

关键词： contrastive learning contrastive loss function Anomaly detection Machine learning Abnormality in time series

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contrastive learning Network for Unsupervised Graph Matching

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第1期35卷 643-656页

作者： Xie, Yu Luo, Lianhang Cao, Tianpei Yu, Bin Qin, A. K. Shanxi Univ Minist Educ Key Lab Computat Intelligence & Chinese Informat P Taiyuan 030006 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China Swinburne Univ Technol Dept Comp Technol Hawthorn Vic 3122 Australia

Graph matching aims to establish node correspondences between graphs, which is a classic combinatorial optimization problem. In recent years, (deep) learning-based methods have emerged as a superior alternative to traditional graph matching solvers. However, these methods typically rely on node-level correspondence labels, which can be prohibitively expensive or unrealistic. Inspired by contrastive learning that is a prevalent paradigm for self-supervised representation learning, we develop a contrastive learning Network for Unsupervised Graph Matching (CUGM), which is an end-to-end differentiable pipeline to learn node permutations. Specifically, we propose three-level augmentation including raw image augmentation, graph augmentation and model augmentation for generating diverse enough contrastive views to enrich training instances. Then a contrastive learning network is constructed to capture the higher-order structural information in graphs and learn the final node representations for yielding the affinity matrix to directly solve a linear assignment problem. More importantly, we propose a node-level contrastive loss with false negative cancellation for optimizing the whole network to extract the tailored node feature representations to improve graph matching accuracy. Experimental results on standard graph matching benchmarks demonstrate that our end-to-end unsupervised method achieves the competitive performance compared with state-of-the-art supervised and unsupervised graph matching methods.

关键词： contrastive learning Feature extraction Training Noise measurement Unsupervised learning Generators Visualization Self-supervised learning graph matching contrastive learning

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contrastive learning-based joint pre-training for unsupervised domain adaptive person re-identification

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MULTIMEDIA SYSTEMS 2025年第2期31卷 1-15页

作者： Wang, Jing Li, Xiaohong Dai, Xuesong Zhuang, Shuo Qi, Meibin Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Anhui Peoples R China

The goal of person re-identification (ReID) is to identify and retrieve images of a specific individual across multiple camera views. To reduce manual annotations, the unsupervised domain adaptive (UDA) person ReID task utilizes labeled data from the source domain to learn unlabeled data from the target domain. Existing clustering-based UDA person ReID methods typically involve two steps. First, the initial model is pre-trained using labeled samples from the source domain. Second, the pre-trained model is fine-tuned using pseudo labels generated through feature clustering, along with the corresponding target samples. However, the significant differences between different domains prevent the pre-trained model from extracting valid feature representations of the target samples. Consequently, the generated pseudo labels from clustering are often inaccurate and not conducive to model fine-tuning. We propose a UDA person ReID method based on joint pre-training. Firstly, we jointly pre-train the model using source and target domain data. A contrastive learning method based on the class-level and cluster-level memory dictionary is used to enable the model to learn feature representations with certain generalizations and discrimination, which is beneficial for generating more accurate pseudo labels during the subsequent fine-tuning stage. Secondly, we design a combined offline and online knowledge distillation method for model fine-tuning, which is used to avoid the accumulation of feature bias and further improve the model's performance. Extensive experiments conducted on three popular datasets demonstrate the effectiveness of our method across multiple benchmarks.

关键词： contrastive learning Joint pre-training Knowledge distillation Person re-identification

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contrastive learning with Global and Local Representation for Mixed-Type Wafer Defect Recognition

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SENSORS 2025年第4期25卷 1272页

作者： Yin, Shantong Zhang, Yangkun Wang, Rui Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China

Recognizing defect patterns in semiconductor wafer bin maps (WBMs) poses a critical challenge in the integrated circuit (IC) manufacturing industry. The accurate classification and segmentation of these defect patterns are of utmost significance as they are key to tracing the root causes of defects, thereby reducing costs and enhancing both product efficiency and quality. As the manufacturing process grows in complexity, the WBM becomes intricate when multiple defect patterns coexist on a single wafer, making the recognition task increasingly complicated. In addition, traditional supervised learning methods require a large number of labeled samples, which is labor-intensive. In this paper, we present a self-supervised contrastive learning framework for the classification and segmentation of mixed-type WBM defect patterns. Our model incorporates a global module for contrastive learning that captures image-level representations, alongside a local module that targets the comprehension of regional details, which is helpful for the segmentation of defective patterns. Experimental results demonstrate that our model performs effectively in scenarios where there is a limited number of labeled examples and a wealth of unlabeled ones.

关键词： pattern classification and segmentation contrastive learning wafer bin map

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contrastive learning With Enhancing Detailed Information for Pre-Training Vision Transformer

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第1期35卷 219-231页

作者： Liang, Zhuomin Bai, Liang Fan, Jinyu Yang, Xian Liang, Jiye Shanxi Univ Inst Intelligent Informat Proc Taiyuan 030006 Peoples R China Univ Manchester Alliance Manchester Business Sch Manchester M13 9PL England

contrastive learning (CL) is an effective self-supervised learning method. It performs instance-level contrastiveness based on the image representations, which enables the model to extract abstract information from images. However, when training data is insufficient, abstract information fails to distinguish samples from different classes. This problem is more severe in the pre-training of Vision Transformer (ViT). In general, detailed information is crucial for enhancing the discrimination of representations. Patch representations, which focus on the details of images, are often overlooked in existing methods that train ViT through CL, resulting in the confusion of similar samples. To address this problem, we propose a contrastive learning model with Enhancing Detailed Information (CL-EDI) for pre-training ViT. Our model consists of dual ViT contrastive modules. The first module is similar to MoCo V3, which can learn abstract information about images. The role of the second ViT contrastive module is to enhance detailed information in data representations by aggregating patch representations of images. Extensive experiments demonstrate the necessity of learning detailed information. Across several datasets, our model surpasses existing approaches in image classification, transfer learning and object detection ***

关键词： contrastive learning Image representation Transformers Training Computer vision Feature extraction Supervised learning vision transformer representation learning

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contrastive learning distillation of non-target categories for facial expression recognition

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INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2025年 1-13页

作者： An, Heng-Yu Jia, Rui-Sheng Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

Facial Expression Recognition (FER) refers to the automated analysis of human emotional states using computer vision techniques, which are of significant importance in tasks such as fatigued driving detection, learning engagement analysis, and safety monitoring. In recent years, deep learning methods have made remarkable progress in facial expression recognition tasks. However, due to scarce samples, high intra-class variation, and inter-class similarity, current approaches perform poorly in recognizing difficult categories, such as Disgust and Fear. To alleviate the performance degradation resulting from these difficult categories, we propose a training framework called contrastive learning Distillation of Non-target Categories (CLDN). The proposed method consists of two stages: In the first stage, the model is jointly trained using contrastive learning and supervised learning to weaken the influence of imbalanced labels on visual representation learning, thereby generating robust soft labels. In the second stage, simple category knowledge is transferred to the difficult categories through self-distillation. During the distillation phase, a Non-target Categories Distillation Loss is introduced, alleviating the inhibition of classic distillation losses on non-target category knowledge and further promoting knowledge transfer during the distillation process. The proposed approach achieves competitive results on the RAF-DB, FERPlus, and AffectNet datasets. Ablation studies demonstrate that the method significantly improves the network's recognition accuracy for difficult categories. The code is available at https://***/Greysahy/CLDN.

关键词： Facial expression recognition contrastive learning Knowledge distillation

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contrastive learning with adversarial masking for sequential recommendation

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2025年 71卷

作者： Xiang, Rongzheng Huang, Jiajin Yang, Jian Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Beijing Int Collaborat Base Brain Informat & Wisdo Beijing 100124 Peoples R China

Sequential recommendation is of paramount importance for predicting user preferences based on their historical interactions. Recent studies have leveraged contrastive learning as an auxiliary task to enhance sequence representations, with the goal of improving recommendation accuracy. However, an important challenge arises: random item masking, a key component of contrastive learning, while promoting robust representations through intricate semantic inference, may inadvertently distort the original sequence semantics to some extent. In contrast, methods that prioritize the preservation of sequence semantics tend to neglect the essential masking mechanism for robust representation learning. To address this issue, we propose a model called contrastive learning with Adversarial Masking (CLAM) for sequential recommendation. CLAM consists of three core components: an inference module, an occlusion module, and a multi-task learning paradigm. During training, the occlusion module is optimized to perturb the inference module in both recommendation generation and contrastive learning tasks by adaptively generating item embedding masks. This adversarial training framework enables CLAM to balance sequential pattern preservation with the acquisition of robust representations in the inference module for recommendation tasks. Our extensive experiments on four benchmark datasets demonstrate the effectiveness of CLAM. It achieves significant improvements in sequential recommendation accuracy and robustness against noisy interactions.

关键词： Sequential recommendation contrastive learning Adversarial learning Data augmentation Masking

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contrastive learning-based multi-view clustering for incomplete multivariate time series

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INFORMATION FUSION 2025年 117卷

作者： Li, Yurui Du, Mingjing Jiang, Xiang Zhang, Nan Jiangsu Normal Univ Sch Comp Sci & Technol Jiangsu Key Lab Educ Intelligent Technol Xuzhou 221116 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

Incomplete multivariate time series (MTS) clustering is a prevalent research topic in time series analysis, aimed at partitioning MTS containing missing data into distinct clusters. contrastive learning-based multi-view clustering methods are a promising approach to address this issue. However, existing methods are typically not designed for time series. Specifically, most of these methods struggle to capture the inherent properties of time series, and are susceptible to losing their interdimensional correlations, thereby compromising data integrity. Furthermore, they commonly utilize data augmentation techniques to generate sample pairs for contrastive learning. These existing data augmentation techniques are not suitable for time series, and introduce uncertainty factors, which can diminish the representation learning capacity of contrastive learning. To address the challenges, we propose a contrastive learning-based multi-view clustering method for incomplete multivariate time series (MVCIMTS). In this method, each variable within the MTS is treated as a separate view, enabling a multi-view learning approach. To better leverage the intrinsic information of time series, we utilize a GRU-based model architecture that integrates imputation and clustering within a unified deep learning framework. In this way, missing views can be effectively inferred, and representations suitable for clustering can be learned, thereby enhancing the clustering performance for incomplete time series. Furthermore, we introduce an innovative contrastive learning approach specifically tailored for MTS, which ensures that the exploration of common semantics and clustering consistency across views remains unaffected by uncertainty factors. It assumes that each time series variable within the same sample has similar representations, thereby taking into account the correlation between variables and enhancing the quality of the representations. To the best of our knowledge, this is the first a

关键词： Multi-view clustering Multivariate time series Missing data contrastive learning Deep learning

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contrastive learning fault diagnosis method for winder chuck based on adaptive sample augmentation and pseudo-label correction

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NONDESTRUCTIVE TESTING AND EVALUATION 2025年

作者： Zhao, Xueyi Wang, Junliang Ren, Jie Cai, Yun Ji, Cheng Donghua Univ Inst Artificial Intelligence Engn Res Ctr Artificial Intelligence Text Ind Minist Educ Shanghai Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China Donghua Univ Coll Mech Engn Shanghai Peoples R China Wuxi 1 Cotton Mill Text Grp Wuxi Jiangsu Peoples R China

In the chemical fibre production process, the winding stage has a significant impact on the final fibre quality. Ensuring accurate and reliable fault diagnosis of the winder is crucial for maintaining production efficiency. However, current fault diagnosis of winder chucks faces the challenge of a lack of effectively labelled fault data. contrastive learning, as an effective solution, has been gradually applied. However, existing contrastive learning frameworks have not been specifically designed for vibration signals of rotating equipment, where spindle vibration signals exhibit high similarity between samples and strong background noise. To address these challenges, this paper first proposes a multi-layer wavelet attention convolutional signal adaptive sample augmentation method to generate diverse positive sample pairs, in order to accommodate the actual working conditions of vibration signals. Secondly, a progressive pseudo-negative sample correction contrastive learning method is proposed to resolve the sampling bias caused by incorrectly treating samples with the same actual label as negative samples. Experimental results demonstrate that the proposed method outperforms existing methods in fault diagnosis accuracy. Further diagnostic experiments on vibration signals from a winder in a chemical fibre plant validate the strong adaptability of the proposed method in real-world operational scenarios.

关键词： Winder fault diagnosis contrastive learning few-shot

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contrastive learning-Based Secure Unsupervised Domain Adaptation Framework and its Application in Cross-Factory Intelligent Manufacturing

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 5106-5113页

作者： Liu, Zeyi Gui, Weihua Huang, Keke Wu, Dehao Liao, Yue Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100083 Peoples R China

Machine learning has been widely applied in industrial intelligent manufacturing. However, significant domain differences in data across factories make it difficult for models trained on a single factory dataset to achieve cross-factory reuse. Unsupervised Domain Adaptation is a method to address this issue, but its basic assumption is the source domain data is available. With increasing attention to data and internet security in the modern manufacturing industry, privacy protection of source data makes it unavailable. To address this challenge, we propose a contrastive learning-based secure unsupervised domain adaptation framework, which does not require source domain data and can achieve high-precision domain alignment by relying on the source domain well-trained model and the target domain unlabeled data. We conduct sufficient experimental studies on a digital recognition benchmark transfer task and a real industrial case, demonstrating that the proposed method outperforms state-of-the-art methods in terms of performance. It is worth mentioning that the proposed method can eliminate the dependence on source domain data, effectively ensuring cross-factory data privacy protection and providing new possibilities for intelligent networked collaborative manufacturing.

关键词： Adaptation models Data models Mathematical models Feature extraction Training Wheels Manufacturing Production facilities Optimization contrastive learning Intelligent manufacturing unsupervised domain adaptation contrastive learning

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