With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is vio...With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.展开更多
Time series with missing values are ubiquitous in healthcare scenarios,presenting significant challenges for analysis.Despite existing methods addressing imputation,they predominantly focus on leveraging intra-series ...Time series with missing values are ubiquitous in healthcare scenarios,presenting significant challenges for analysis.Despite existing methods addressing imputation,they predominantly focus on leveraging intra-series information,neglecting the potential benefits that inter-series information could provide,such as reducing uncertainty and memorization effect.To bridge this gap,we propose PRIME,Prototype Recurrent Imputation ModEl,which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series.PRIME comprises a prototype memory module for learning inter-series information,a bidirectional gated recurrent unit utilizing prototype information for imputation,and an attentive prototypical refinement module for adjusting imputations.We conduct extensive experiments on four datasets,and the results underscore PRIME’s superiority over the state-of-the-art models by up to 26%relative improvement in mean square error.Our code is available at https://jcst.ict.ac.cn/news/382.展开更多
Training generative adversarial networks is data-demanding,which limits the development of these models on target domains with inadequate training data.Recently,researchers have leveraged generative models pretrained ...Training generative adversarial networks is data-demanding,which limits the development of these models on target domains with inadequate training data.Recently,researchers have leveraged generative models pretrained on sufficient data and fine-tuned them using small training samples,thus reducing data requirements.However,due to the lack of explicit focus on target styles and disproportionately concentrating on generative consistency,these methods do not perform well in diversity preservation which represents the adaptation ability for few-shot generative models.To mitigate the diversity degradation,we propose a framework with two key strategies:1)To obtain more diverse styles from limited training data effectively,we propose a cross-modal module that explicitly obtains the target styles with a style prototype space and text-guided style instructions.2)To inherit the generation capability from the pretrained model,we aim to constrain the similarity between the generated and source images with a structural discrepancy alignment module by maintaining the structure correlation in multiscale areas.We demonstrate the effectiveness of our method,which outperforms state-of-the-art methods in mitigating diversity degradation through extensive experiments and analyses.展开更多
Knowledge distillation has demonstrated considerable success in scenarios involving multi-class single-label learning.However,its direct application to multi-label learning proves challenging due to complex correlatio...Knowledge distillation has demonstrated considerable success in scenarios involving multi-class single-label learning.However,its direct application to multi-label learning proves challenging due to complex correlations in multi-label structures,causing student models to overlook more finely structured semantic relations present in the teacher model.In this paper,we present a solution called multi-label prototype-aware structured contrastive distillation,comprising two modules:Prototype-aware Contrastive Representation Distillation(PCRD)and prototype-aware cross-image structure distillation.The PCRD module maximizes the mutual information of prototype-aware representation between the student and teacher,ensuring semantic representation structure consistency to improve the compactness of intra-class and dispersion of inter-class representations.In the PCSD module,we introduce sample-to-sample and sample-to-prototype structured contrastive distillation to model prototype-aware cross-image structure consistency,guiding the student model to maintain a coherent label semantic structure with the teacher across multiple instances.To enhance prototype guidance stability,we introduce batch-wise dynamic prototype correction for updating class prototypes.Experimental results on three public benchmark datasets validate the effectiveness of our proposed method,demonstrating its superiority over state-of-the-art methods.展开更多
Recently,large-scale pretrained models have revealed their benefits in various tasks.However,due to the enormous computation complexity and storage demands,it is challenging to apply large-scale models to real scenari...Recently,large-scale pretrained models have revealed their benefits in various tasks.However,due to the enormous computation complexity and storage demands,it is challenging to apply large-scale models to real scenarios.Existing knowledge distillation methods require mainly the teacher model and the student model to share the same label space,which restricts their application in real scenarios.To alleviate the constraint of different label spaces,we propose a prototype-guided cross-task knowledge distillation(ProC-KD)method to migrate the intrinsic local-level object knowledge of the teacher network to various task scenarios.First,to better learn the generalized knowledge in cross-task scenarios,we present a prototype learning module to learn the invariant intrinsic local representation of objects from the teacher network.Second,for diverse downstream tasks,a task-adaptive feature augmentation module is proposed to enhance the student network features with the learned generalization prototype representations and guide the learning of the student network to improve its generalization ability.Experimental results on various visual tasks demonstrate the effectiveness of our approach for cross-task knowledge distillation scenarios.展开更多
Predicting future heart rate(HR)not only helps in detecting abnormal heart rhythms but also provides timely support for downstream health monitoring services.Existing methods for HR prediction encounter challenges,esp...Predicting future heart rate(HR)not only helps in detecting abnormal heart rhythms but also provides timely support for downstream health monitoring services.Existing methods for HR prediction encounter challenges,especially concerning privacy protection and data heterogeneity.To address these challenges,this paper proposes a novel HR prediction framework,PCFedH,which leverages personalized federated learning and prototypical contrastive learning to achieve stable clustering results and more accurate predictions.PCFedH contains two core modules:a prototypical contrastive learning-based federated clustering module,which characterizes data heterogeneity and enhances HR representation to facilitate more effective clustering,and a two-phase soft clustered federated learning module,which enables personalized performance improvements for each local model based on stable clustering results.Experimental results on two real-world datasets demonstrate the superiority of our approach over state-of-the-art methods,achieving an average reduction of 3.1%in the mean squared error across both datasets.Additionally,we conduct comprehensive experiments to empirically validate the effectiveness of the key components in the proposed method.Among these,the personalization component is identified as the most crucial aspect of our design,indicating its substantial impact on overall performance.展开更多
基金supported by National Natural Science Foundation of China(No.52277083).
文摘With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.
基金supported by the National Natural Science Foundation of China under Grant Nos.62402017 and U23A20468the Beijing Natural Science Foundation under Grant Nos.L244063 and L244025+2 种基金the Peking University Clinical Medicine Plus X(Young Scholars Project-the Fundamental Research Funds for the Central Universities of China under Grant No.PKU2025PKULCXQ024the Pilot Program-Key Technologies Project under Grant No.2024YXXLHGG007)the Xuzhou Scientific Technological Projects under Grant No.KC23143.
文摘Time series with missing values are ubiquitous in healthcare scenarios,presenting significant challenges for analysis.Despite existing methods addressing imputation,they predominantly focus on leveraging intra-series information,neglecting the potential benefits that inter-series information could provide,such as reducing uncertainty and memorization effect.To bridge this gap,we propose PRIME,Prototype Recurrent Imputation ModEl,which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series.PRIME comprises a prototype memory module for learning inter-series information,a bidirectional gated recurrent unit utilizing prototype information for imputation,and an attentive prototypical refinement module for adjusting imputations.We conduct extensive experiments on four datasets,and the results underscore PRIME’s superiority over the state-of-the-art models by up to 26%relative improvement in mean square error.Our code is available at https://jcst.ict.ac.cn/news/382.
基金supported by the National Key Research and Development Program of China,China(No.2021YFC3320103)the National Natural Science Foundation of China,China(NSFC)(Nos.62372452 and 62272460)+1 种基金the Open Research Project of the State Key Laboratory of Media Convergence and Communication,Communication University of China,China(No.SKLM CC2022KF002)Youth Innovation Promotion Association CAS,China.
文摘Training generative adversarial networks is data-demanding,which limits the development of these models on target domains with inadequate training data.Recently,researchers have leveraged generative models pretrained on sufficient data and fine-tuned them using small training samples,thus reducing data requirements.However,due to the lack of explicit focus on target styles and disproportionately concentrating on generative consistency,these methods do not perform well in diversity preservation which represents the adaptation ability for few-shot generative models.To mitigate the diversity degradation,we propose a framework with two key strategies:1)To obtain more diverse styles from limited training data effectively,we propose a cross-modal module that explicitly obtains the target styles with a style prototype space and text-guided style instructions.2)To inherit the generation capability from the pretrained model,we aim to constrain the similarity between the generated and source images with a structural discrepancy alignment module by maintaining the structure correlation in multiscale areas.We demonstrate the effectiveness of our method,which outperforms state-of-the-art methods in mitigating diversity degradation through extensive experiments and analyses.
基金supported by the National Natural Science Foundation of China(No.62466061)the Yunnan Fundamental Research Projects(No.202401AU070052)+1 种基金the Yunnan Provincial Department of Education Science Research Fund,China(Nos.2023J0209 and 2024Y161)the Natural Science Doctoral Research Start-Up Fund of Yunnan Normal University(No.2022ZB015).
文摘Knowledge distillation has demonstrated considerable success in scenarios involving multi-class single-label learning.However,its direct application to multi-label learning proves challenging due to complex correlations in multi-label structures,causing student models to overlook more finely structured semantic relations present in the teacher model.In this paper,we present a solution called multi-label prototype-aware structured contrastive distillation,comprising two modules:Prototype-aware Contrastive Representation Distillation(PCRD)and prototype-aware cross-image structure distillation.The PCRD module maximizes the mutual information of prototype-aware representation between the student and teacher,ensuring semantic representation structure consistency to improve the compactness of intra-class and dispersion of inter-class representations.In the PCSD module,we introduce sample-to-sample and sample-to-prototype structured contrastive distillation to model prototype-aware cross-image structure consistency,guiding the student model to maintain a coherent label semantic structure with the teacher across multiple instances.To enhance prototype guidance stability,we introduce batch-wise dynamic prototype correction for updating class prototypes.Experimental results on three public benchmark datasets validate the effectiveness of our proposed method,demonstrating its superiority over state-of-the-art methods.
基金Project supported by the National Natural Science Foundation of China(Nos.62376186 and 61932009)。
文摘Recently,large-scale pretrained models have revealed their benefits in various tasks.However,due to the enormous computation complexity and storage demands,it is challenging to apply large-scale models to real scenarios.Existing knowledge distillation methods require mainly the teacher model and the student model to share the same label space,which restricts their application in real scenarios.To alleviate the constraint of different label spaces,we propose a prototype-guided cross-task knowledge distillation(ProC-KD)method to migrate the intrinsic local-level object knowledge of the teacher network to various task scenarios.First,to better learn the generalized knowledge in cross-task scenarios,we present a prototype learning module to learn the invariant intrinsic local representation of objects from the teacher network.Second,for diverse downstream tasks,a task-adaptive feature augmentation module is proposed to enhance the student network features with the learned generalization prototype representations and guide the learning of the student network to improve its generalization ability.Experimental results on various visual tasks demonstrate the effectiveness of our approach for cross-task knowledge distillation scenarios.
基金supported by the National Natural Science Foundation of China(Nos.62102094 and 62072115)the Shanghai Science and Technology Innovation Action Plan Project(No.22510713600)the NIO University Programme,and the Nordic University Cooperation on Edge Intelligence(No.168043)。
文摘Predicting future heart rate(HR)not only helps in detecting abnormal heart rhythms but also provides timely support for downstream health monitoring services.Existing methods for HR prediction encounter challenges,especially concerning privacy protection and data heterogeneity.To address these challenges,this paper proposes a novel HR prediction framework,PCFedH,which leverages personalized federated learning and prototypical contrastive learning to achieve stable clustering results and more accurate predictions.PCFedH contains two core modules:a prototypical contrastive learning-based federated clustering module,which characterizes data heterogeneity and enhances HR representation to facilitate more effective clustering,and a two-phase soft clustered federated learning module,which enables personalized performance improvements for each local model based on stable clustering results.Experimental results on two real-world datasets demonstrate the superiority of our approach over state-of-the-art methods,achieving an average reduction of 3.1%in the mean squared error across both datasets.Additionally,we conduct comprehensive experiments to empirically validate the effectiveness of the key components in the proposed method.Among these,the personalization component is identified as the most crucial aspect of our design,indicating its substantial impact on overall performance.
