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MoCoUTRL: a momentum contrastive framework for unsupervised text representation learning
Connection Science, 2023 This paper presents MoCoUTRL: a Momentum Contrastive Framework for Unsupervised Text Representation Learning. This model improves two aspects of recently popular contrastive learning algorithms in natural language processing (NLP).
Ao Zou +4 more
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A novel fractional-order flocking algorithm for large-scale UAV swarms
Complex & Intelligent Systems, 2023 The rate of convergence is a vital factor in determining the outcome of the mission execution of unmanned aerial vehicle (UAV) swarms. However, the difficulty of developing a rapid convergence strategy increases dramatically with the growth of swarm ...
Haotian Chen +6 more
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A Multiview Metric Learning Method for Few-Shot Fine-Grained Classification
IEEE Access, 2022 Few-shot fine-grained image classification aims to solve the learning problem with few limited labeled examples. The existing methods use data augmentation to randomly transform the original examples to get new examples, and then use the new examples to ...
Zhuang Miao +5 more
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Multi‐task few‐shot learning with composed data augmentation for image classification
IET Computer Vision, 2023 Few‐shot learning (FSL) attempts to learn and optimise the model from a few examples on image classification, which is still threatened by data scarcity.
Rui Zhang +5 more
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Joint Attention Mechanism for Person Re-Identification
IEEE Access, 2019 Although person re-identification (ReID) has drawn increasing research attention due to its potential to address the problem of analysis and processing of massive monitoring data, it is very challenging to learn discriminative information when the people
Shanshan Jiao +5 more
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Adaptive Service Function Chain Scheduling in Mobile Edge Computing via Deep Reinforcement Learning
IEEE Access, 2020 MEC (Mobile Edge Computing) provides both IT service environment and cloud computation on the edge of the network. This technology not only minimizes the end-to-end latency but also increases the efficiency of computing.
Tianfeng Wang +3 more
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How to Make Attention Mechanisms More Practical in Malware Classification
IEEE Access, 2019 Malware and its variants continue to pose a threat to network security. Machine learning has been widely used in the field of malware classification, but some emerging studies, such as attention mechanisms, are rarely applied in this field. In this paper,
Xin Ma +6 more
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Contrastive Self-Supervised Hashing With Dual Pseudo Agreement
IEEE Access, 2020 Recently, unsupervised deep hashing has attracted increasing attention, mainly because of its potential ability to learn binary codes without identity annotations.
Yang Li +4 more
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IEEE Access, 2019 With the development of network technology such as software-defined network (SDN) and network function virtualization (NFV), Internet service providers (ISPs) are increasingly placing the virtual network function(VNF) instances at the network edge to ...
Jiachen Zu +4 more
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Ontology Alignment Repair Through 0-1 Programming
IEEE Access, 2019 In order to solve the problem of lack of effective methods for ontology inconsistency, a user preferences-oriented ontology alignment repair model is proposed.
Wenning Hao +5 more
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