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Wang, H., Zhuang, L., Ding, Y., Tiwari, P. & Liang, C. (2025). EDDINet: Enhancing drug–drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning. Artificial Intelligence in Medicine, 159, 1-13, Article ID 103029.
Open this publication in new window or tab >>EDDINet: Enhancing drug–drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning
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2025 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 159, p. 1-13, article id 103029Article in journal (Refereed) Published
Abstract [en]

Predicting drug–drug interactions (DDIs) is crucial for understanding and preventing adverse drug reactions (ADRs). However, most existing methods inadequately explore the interactive information between drugs in a self-supervised manner, limiting our comprehension of drug–drug associations. This paper introduces EDDINet: Enhancing Drug-Drug Interaction Prediction via Information Flow and Consensus-Constrained Multi-Graph Contrastive Learning for precise DDI prediction. We first present a cross-modal information-flow mechanism to integrate diverse drug features, enriching the structural insights conveyed by the drug feature vector. Next, we employ contrastive learning to filter various biological networks, enhancing the model's robustness. Additionally, we propose a consensus regularization framework that collaboratively trains multi-view models, producing high-quality drug representations. To unify drug representations derived from different biological information, we utilize an attention mechanism for DDI prediction. Extensive experiments demonstrate that EDDINet surpasses state-of-the-art unsupervised models and outperforms some supervised baseline models in DDI prediction tasks. Our approach shows significant advantages and holds promising potential for advancing DDI research and improving drug safety assessments. Our codes are available at: https://github.com/95LY/EDDINet_code. © 2024 The Authors

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2025
Keywords
Consensus regularization, Contrastive learning, DDI prediction, Information flow, Multi-graph
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hh:diva-55055 (URN)10.1016/j.artmed.2024.103029 (DOI)2-s2.0-85210116931 (Scopus ID)
Note

This work is supported by the National Natural Science Foundation of China (No. 62072290 , 61672329 , 62172076 ), Natural Science Foundation of Shandong Province (No. ZR2021MF118 , ZR2022QF022 ), Postgraduate Quality Education and Teaching Resources Project of Shandong Province (No. SDYKC2022053 , SDYAL2022060 ), Jinan \u201C20 new colleges and universities\u201D Funded Project (No. 202228110 ), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY23F020003 ), and the Municipal Government of Quzhou ( 2022D006 ).

Available from: 2024-12-10 Created: 2024-12-10 Last updated: 2024-12-10Bibliographically approved
Zhai, M., Wu, Q., Liu, Y., Qin, B., Yang, Y., Muhammad, G. & Tiwari, P. (2025). Privacy Preservation in AI-Driven IoT for Vehicles via Hierarchical Sharding Blockchain. IEEE Internet of Things Journal, 1-15
Open this publication in new window or tab >>Privacy Preservation in AI-Driven IoT for Vehicles via Hierarchical Sharding Blockchain
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2025 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, p. 1-15Article in journal (Refereed) Epub ahead of print
Abstract [en]

The AI-driven Internet of Things (AIoT) has been widely applied in the field of Internet of Vehicles (IoV) for vehicular cooperation. Federated Learning (FL), due to its ability to protect users' data privacy, reduce communication overhead, and facilitate real-time decision-making, is widely applied in the augmented intelligence of things for vehicles (AIoV). However, integrating FL with AIoV poses challenges, including the absence of fine-grained access control, insufficient safeguards for FL tasks and vehicle identities, inadequate security for data transmission, and shortcomings in protecting data storage. These vulnerabilities may lead to risks such as vehicle tracking, model information theft, and data tampering. To address these challenges, we propose a privacy preservation mechanism for AIoV via cloud-edge-vehicle hierarchical sharding blockchain. Firstly, we propose a hierarchical anonymous authentication scheme for IoV devices with stronger scalability and higher fault tolerance. Vehicles only know the attributes of each other or which shard they belong to. Secondly, we present a secure FL task assignment scheme for AIoV. Edge nodes utilize attribute-based encryption to deploy fine-grained FL tasks based on vehicle attributes. Only users who meet the attributes can decrypt the content, protecting FL tasks content and participant identities. Thirdly, we present a secure data transmission scheme between AIoV devices to protect the identity and data privacy of both parties, while also achieving non-interactive key agreement. Additionally, we propose a scalable secure data sharing and storage scheme based on hierarchical sharding blockchain, aiming to reduce storage overhead and minimize trust costs. © 2014 IEEE.

Place, publisher, year, edition, pages
Piscataway: IEEE, 2025
Keywords
augmented intelligence for vehicles, authentication, blockchain, data privacy protection, Internet of Vehicles
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-55182 (URN)10.1109/JIOT.2024.3513770 (DOI)2-s2.0-85212285681 (Scopus ID)
Available from: 2025-01-07 Created: 2025-01-07 Last updated: 2025-01-07Bibliographically approved
Qu, Z., Zhang, L. & Tiwari, P. (2025). Quantum Fuzzy Federated Learning for Privacy Protection in Intelligent Information Processing. IEEE transactions on fuzzy systems, 33(1), 278-289
Open this publication in new window or tab >>Quantum Fuzzy Federated Learning for Privacy Protection in Intelligent Information Processing
2025 (English)In: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 33, no 1, p. 278-289Article in journal (Refereed) Published
Abstract [en]

With the advent of the intelligent information processing era, more and more private sensitive data are being collected and analyzed for intelligent decision making tasks. Such information processing also brings many challenges with existing privacy protection algorithms. On the one hand, the algorithms based on data encryption compromise the integrity of the original data or incur high computational and communication costs to some extent. On the other hand, algorithms based on distributed learning require frequent sharing of parameters between different computing nodes, which poses risks of leaking local model information and reducing global learning efficiency. To mitigate the impact of these issues, a Quantum Fuzzy Federated Learning (QFFL) algorithm is proposed. In the QFFL algorithm, a Quantum Fuzzy Neural Network (QFNN) is designed at the local computing nodes, which enhances data generalization while preserving data integrity. In global model, QFFL makes predictions through the Quantum Federated Inference (QFI). QFI leads to a general framework for quantum federated learning on non-IID data with oneshot communication complexity, achieving privacy protection of local data and accelerating the global learning efficiency of the algorithm. The experiments are conducted on the COVID19 and MNIST datasets, and the results indicate that QFFL demonstrates superior performance compared to the baselines, manifesting in faster training efficiency, higher accuracy, and enhanced security. In addition, based on the fidelity experiments and related analysis under four common quantum noise channels, the results demonstrated that it has good robustness against quantum noises, proving its applicability and practicality. Our code is available at https://github.com/LASTsue/QFFL. © IEEE

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2025
Keywords
Computational modeling, Data privacy, Distance learning, Fuzzy neural networks, Intelligent information processing, Privacy, Privacy protection, Protection, Quantum computing, Quantum federated inference, Quantum fuzzy federated learning, Quantum fuzzy neural network
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-54350 (URN)10.1109/TFUZZ.2024.3419559 (DOI)001394760500007 ()2-s2.0-85197058655 (Scopus ID)
Available from: 2024-08-01 Created: 2024-08-01 Last updated: 2025-02-05Bibliographically approved
Zhang, C., Su, Z., Li, Q., Song, D. & Tiwari, P. (2025). Quantum-inspired neural network with hierarchical entanglement embedding for matching. Neural Networks, 182, 1-16, Article ID 106915.
Open this publication in new window or tab >>Quantum-inspired neural network with hierarchical entanglement embedding for matching
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2025 (English)In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 182, p. 1-16, article id 106915Article in journal (Refereed) In press
Abstract [en]

Quantum-inspired neural networks (QNNs) have shown potential in capturing various non-classical phenomena in language understanding, e.g., the emgerent meaning of concept combinations, and represent a leap beyond conventional models in cognitive science. However, there are still two limitations in the existing QNNs: (1) Both storing and invoking the complex-valued embeddings may lead to prohibitively expensive costs in memory consumption and storage space. (2) The use of entangled states can fully capture certain non-classical phenomena, which are described by the tensor product with powerful compression ability. This approach shares many commonalities with the process of word formation from morphemes, but such connection has not been further exploited in the existing work. To mitigate these two limitations, we introduce a Quantum-inspired neural network with Hierarchical Entanglement Embedding (QHEE) based on finer-grained morphemes. Our model leverages the intra-word and inter-word entanglement embeddings to learn a multi-grained semantic representation. The intra-word entanglement embedding is employed to aggregate the constituent morphemes from multiple perspectives, while the inter-word entanglement embedding is utilized to combine different words based on unitary transformation to reveal their non-classical correlations. Both the number of morphemes and the dimensionality of the morpheme embedding vectors are far smaller than the counterparts of words, which would compress embedding parameters efficiently. Experimental results on four benchmark datasets of different downstream tasks show that our model outperforms strong quantum-inspired baselines in terms of effectiveness and compression ability. © 2024 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2025
Keywords
Cognitive computation, Complex-valued neural networks, Entanglement embedding, Matching, Quantum-like machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-55052 (URN)10.1016/j.neunet.2024.106915 (DOI)001373088300001 ()39612690 (PubMedID)2-s2.0-85210125989 (Scopus ID)
Note

Funding agency: Beijing Municipal Natural Foundation. Grant number: IS23061, 4222036

Available from: 2024-12-13 Created: 2024-12-13 Last updated: 2024-12-20Bibliographically approved
Zhang, C., Li, Q., Song, D. & Tiwari, P. (2025). Quantum-inspired semantic matching based on neural networks with the duality of density matrices. Engineering applications of artificial intelligence, 140, 1-15, Article ID 109667.
Open this publication in new window or tab >>Quantum-inspired semantic matching based on neural networks with the duality of density matrices
2025 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 140, p. 1-15, article id 109667Article in journal (Refereed) Published
Abstract [en]

Social media text can be semantically matched in different ways, viz paraphrase identification, answer selection, community question answering, and so on. The performance of the above semantic matching tasks depends largely on the ability of language modeling. Neural network based language models and probabilistic language models are two main streams of language modeling approaches. However, few prior work has managed to unify them in a single framework on the premise of preserving probabilistic features during the neural network learning process. Motivated by recent advances of quantum-inspired neural networks for text representation learning, we fill the gap by resorting to density matrices, a key concept describing a quantum state as well as a quantum probability distribution. The state and probability views of density matrices are mapped respectively to the neural and probabilistic aspects of language models. Concretizing this state-probability duality to the semantic matching task, we build a unified neural-probabilistic language model through a quantum-inspired neural network. Specifically, we take the state view to construct a density matrix representation of sentence, and exploit its probabilistic nature by extracting its main semantics, which form the basis of a legitimate quantum measurement. When matching two sentences, each sentence is measured against the main semantics of the other. Such a process is implemented in a neural structure, facilitating an end-to-end learning of parameters. The learned density matrix representation reflects an authentic probability distribution over the semantic space throughout the training process. Experiments show that our model significantly outperforms a wide range of prominent classical and quantum-inspired baselines. © 2024 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2025
Keywords
Complex-valued neural network, Density matrix, Neural network, Quantum theory, State-probability duality
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-55053 (URN)10.1016/j.engappai.2024.109667 (DOI)2-s2.0-85210363293 (Scopus ID)
Note

This work is funded in part by the Beijing Municipal Natural Science Foundation (grant no: IS23061 and 4222036 ) and Natural Science Foundation of China (grant no: 62376027 ).

Available from: 2024-12-13 Created: 2024-12-13 Last updated: 2024-12-13Bibliographically approved
Xing, X., Wang, B., Ning, X., Wang, G. & Tiwari, P. (2025). Short-term OD flow prediction for urban rail transit control: A multi-graph spatiotemporal fusion approach. Information Fusion, 118, Article ID 102950.
Open this publication in new window or tab >>Short-term OD flow prediction for urban rail transit control: A multi-graph spatiotemporal fusion approach
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2025 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 118, article id 102950Article in journal (Refereed) In press
Abstract [en]

There is growing pressure to manage and run urban rail transit networks as more people select this mode of transportation for their travel. It is, therefore, essential to create a precise system to predict passenger movements from origins to destinations (OD). The effectiveness of the present methods in simulating links between stations using real-time passenger flow data is limited. This paper suggests a Spatiotemporal Fusion Network for OD flow forecasting in urban rail travel. By examining previous OD data, this network creates spatiotemporal link graphs connecting stations that integrate spatiotemporal correlations of passenger flows. These graphs are incorporated into the forecasting system to forecast OD flows. Using actual passenger flow data from Shanghai and Hangzhou, we validate our MGLTN model and show almost 4% improvement in prediction accuracy (measured by MAE) over many state-of-the-art baseline models on both datasets. We also present a dwell score derived from anticipated train frequencies and passenger flows. Each station receives a rating based on this score, which represents its dwell qualities in comparison to predetermined norms. © 2025

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2025
Keywords
Feature fusion, Graph convolutional network, Long short-term memory network, Origin–destination flow prediction, Spatial–temporal dependency, Urban rail transit
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:hh:diva-55379 (URN)10.1016/j.inffus.2025.102950 (DOI)2-s2.0-85215539158 (Scopus ID)
Available from: 2025-02-06 Created: 2025-02-06 Last updated: 2025-02-06Bibliographically approved
Huang, J., Yu, X., An, D., Ning, X., Liu, J. & Tiwari, P. (2025). Uniformity and deformation: A benchmark for multi-fish real-time tracking in the farming. Expert systems with applications, 264, 1-11, Article ID 125653.
Open this publication in new window or tab >>Uniformity and deformation: A benchmark for multi-fish real-time tracking in the farming
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2025 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 264, p. 1-11, article id 125653Article in journal (Refereed) In press
Abstract [en]

In the context of smart aquaculture, real-time Multi-fish Tracking (MFT) poses a significant challenge. Existing Multi-Object Tracking (MOT) methods are often designed for objects with specific shapes or regular motion patterns, such as pedestrians or cars. The unique characteristics of fish, including their uniform appearance and deformation during motion, have been largely overlooked in research. To address this gap, we introduce the Uniform and Deformable Multi-fish Tracking (UD-MFT) benchmark. This dataset not only incorporates challenges related to uniform appearance and diverse deformable shapes of fish during motion in daily activities but also encompasses common MOT challenges like occlusion and disappearance. All sequences are sourced from industrialized aquaculture environments, providing a practical and relevant setting. To understand the distinctiveness of UD-MFT, we quantify the degrees of deformation, appearance, and occlusion levels within the dataset and compare them with tracking targets in existing datasets. Furthermore, to facilitate practical applications, we conduct a comprehensive evaluation of state-of-the-art real-time MOT models on UD-MFT, establishing a comparative baseline for accuracy and computational requirements. Additionally, we perform an in-depth analysis of the impact of deformation and appearance similarity on tracking accuracy. Finally, we provide reflections and recommendations concerning potential avenues for future research in this field. The proposed UD-MFT aims to serve as a robust platform for developing algorithms capable of handling fish with multiple motion patterns, thereby contributing to the advancement of intelligent fish farming. © 2024 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2025
Keywords
Benchmark, Deformable multiple fish tracking, Multi-object tracking, Tracking evaluation
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-55051 (URN)10.1016/j.eswa.2024.125653 (DOI)001370509200001 ()2-s2.0-85210117353 (Scopus ID)
Note

This work was supported by National Key R&D Programs of China (Grant No. 2022YFE0107100).

Available from: 2024-12-10 Created: 2024-12-10 Last updated: 2024-12-10Bibliographically approved
Liu, Y., Liu, A., Xia, Y., Hu, B., Liu, J., Wu, Q. & Tiwari, P. (2024). A Blockchain-Based Cross-Domain Authentication Management System for IoT Devices. IEEE Transactions on Network Science and Engineering, 11(1), 115-127
Open this publication in new window or tab >>A Blockchain-Based Cross-Domain Authentication Management System for IoT Devices
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2024 (English)In: IEEE Transactions on Network Science and Engineering, E-ISSN 2327-4697, Vol. 11, no 1, p. 115-127Article in journal (Refereed) Published
Abstract [en]

With the emergence of the resource and equipment sharing concept, many enterprises and organizations begin to implement cross-domain sharing of devices, especially in the field of the Internet of Things (IoT). However, there are many problems in the cross-domain usage process of devices, such as access control, authentication, and privacy protection. In this paper, we make the following contributions. First, we propose a blockchain-based cross-domain authentication management system for IoT devices. The sensitive device information is stored in a Merkle tree structure where only the Merkle root is uploaded to the smart contract. Second, a detailed security and performance analysis is given. We prove that our system is secure against several potential security threats and satisfies validity and liveness. Compared to existing schemes, our schemes realize decentralization, privacy, scalability, fast off-chain authentication, and low on-chain storage. Third, we implement the system on Ethereum with varying parameters known as domain number, concurrent authentication request number, and Merkle tree leaf number. Experimental results show that our solution supports the management of millions of devices in a domain and can process more than 10,000 concurrent cross-domain authentication requests, consuming only 5531 ms. Meanwhile, the gas costs are shown to be acceptable. © IEEE

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2024
Keywords
Authentication, Blockchains, cross-domain authentication, Internet of Things, IoT device management, Merkle tree, Organizations, Peer-to-peer computing, Scalability, smart contract, Smart contracts
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-51427 (URN)10.1109/TNSE.2023.3292624 (DOI)2-s2.0-85164391552 (Scopus ID)
Note

Funding: National Key R&D Program of China (Grant Number: 2021YFB2700200); Natural Science Foundation of China (Grant Number: U21A20467, U21B2021, U22B2008, U2241213, 62202027, 61932011, 61972019, 61972018, 61972017, 62172025 and 61932014); Young Elite Scientists Sponsorship Program by CAST (Grant Number: 2022QNRC001); Beijing Natural Science Foundation (Grant Number: M23016, M21031, L222050 and M22038); CCF-Huawei Huyanglin Foundation (Grant Number: CCF-HuaweiBC2021009); Yunnan Key Laboratory of Blockchain Application Technology Open Project (Grant Number: 202105AG070005 and YNB202206)

Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2024-01-16Bibliographically approved
Miao, J., Wang, Z., Wu, Z., Ning, X. & Tiwari, P. (2024). A blockchain-enabled privacy-preserving authentication management protocol for Internet of Medical Things. Expert systems with applications, 237, Part A, Article ID 121329.
Open this publication in new window or tab >>A blockchain-enabled privacy-preserving authentication management protocol for Internet of Medical Things
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2024 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 237, Part A, article id 121329Article in journal (Refereed) Published
Abstract [en]

Over the last decade, with the increasing popularity and usage of the internet of things worldwide, Internet of Medical Things (IoMT) has emerged as a key technology of the modern era. IoMT uses Artificial Intelligence, 5G, big data, edge computing, and blockchain to provide users with electronic medical services. However, it may face several security threats and attacks over an insecure public network. Therefore, to protect sensitive medical data in IoMT, it is necessary to design a secure and efficient authentication protocol. In this study, we propose a privacy-preserving authentication management protocol based on blockchain. The protocol uses a blockchain to store identities and related parameters to assist communication entities in authentication. In addition, the protocol adopts a three-factor authentication method and introduces Chebyshev chaotic map to ensure the security of user login and authentication. Formal security proof and analysis using the random oracle model and Burrows-Abadi-Needham logic show that the proposed protocol is secure. Moreover, we use informal security analysis to demonstrate that the protocol can resist various security attacks. The functional comparison shows that the protocol has high security. Through performance analysis and comparison with other protocols, the proposed protocol can increase computation overhead, communication overhead, and storage overhead by up to 39.8%, 93.6%, and 86.7%,respectively. © 2023 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2024
Keywords
Blockchain, Chebyshev chaotic map, Internet of medical things, Privacy-preserving, Three-factor
National Category
Communication Systems
Identifiers
urn:nbn:se:hh:diva-51669 (URN)10.1016/j.eswa.2023.121329 (DOI)001075477100001 ()2-s2.0-85170423010 (Scopus ID)
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2024-01-17Bibliographically approved
Tian, S., Li, L., Li, W., Ran, H., Ning, X. & Tiwari, P. (2024). A survey on few-shot class-incremental learning. Neural Networks, 169, 307-324
Open this publication in new window or tab >>A survey on few-shot class-incremental learning
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2024 (English)In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 169, p. 307-324Article, review/survey (Refereed) Published
Abstract [en]

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective. © 2023 The Author(s)

Place, publisher, year, edition, pages
Oxford: Elsevier, 2024
Keywords
Catastrophic forgetting, Class-incremental learning, Few-shot learning, Overfitting, Performance evaluation
National Category
Computer graphics and computer vision
Identifiers
urn:nbn:se:hh:diva-52064 (URN)10.1016/j.neunet.2023.10.039 (DOI)2-s2.0-85175341096 (Scopus ID)
Note

Funding: This work is supported by the National Natural Science Foundation of China (Grant No. 62373343) and the Beijing Natural Science Foundation, China (No. L233036).

Available from: 2023-11-17 Created: 2023-11-17 Last updated: 2025-02-07Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-2851-4260

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