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IEEE Signal Processing Letters
Hybrid active noise control (ANC) is widely employed in portable commercial products to attenuate both broadband and narrowband noise. Although the adaptive hybrid ANC, updated by the filtered reference least mean square (FxLMS) algorithm, can achieve ...
Xiaoyi Shen, Dongyuan Shi, Woon-Seng Gan
semanticscholar +1 more source
Hybrid active noise control (ANC) is widely employed in portable commercial products to attenuate both broadband and narrowband noise. Although the adaptive hybrid ANC, updated by the filtered reference least mean square (FxLMS) algorithm, can achieve ...
Xiaoyi Shen, Dongyuan Shi, Woon-Seng Gan
semanticscholar +1 more source
CCoMAML: Efficient Cattle Identification Using Cooperative Model-Agnostic Meta-Learning
arXiv.orgCattle identification is critical for efficient livestock farming management, currently reliant on radio-frequency identification (RFID) ear tags. However, RFID-based systems are prone to failure due to loss, damage, tampering, and vulnerability to ...
Rabin Dulal, Lihong Zheng, Ashad Kabir
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Electronics
Palmprint recognition is becoming more and more common in the fields of security authentication, mobile payment, and crime detection. Aiming at the problem of small sample size and low recognition rate of palmprint, a small-sample palmprint recognition ...
Xiancheng Zhou +4 more
semanticscholar +1 more source
Palmprint recognition is becoming more and more common in the fields of security authentication, mobile payment, and crime detection. Aiming at the problem of small sample size and low recognition rate of palmprint, a small-sample palmprint recognition ...
Xiancheng Zhou +4 more
semanticscholar +1 more source
Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning
2020 23rd International Conference on Electrical Machines and Systems (ICEMS), 2020As an essential component of many mission-critical equipment, mechanical bearings need to be monitored to identify any traces of abnormal conditions. Most of the latest data-driven methods applied to bearing anomaly detection are trained using a large amount of fault data collected a priori. However, in many practical applications, it may be unsafe and
Shen Zhang +3 more
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arXiv.org
Meta-learning algorithms enable rapid adaptation to new tasks with minimal data, a critical capability for real-world robotic systems. This paper evaluates Model-Agnostic Meta-Learning (MAML) combined with Trust Region Policy Optimization (TRPO) on the ...
Sanjar Atamuradov
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Meta-learning algorithms enable rapid adaptation to new tasks with minimal data, a critical capability for real-world robotic systems. This paper evaluates Model-Agnostic Meta-Learning (MAML) combined with Trust Region Policy Optimization (TRPO) on the ...
Sanjar Atamuradov
semanticscholar +1 more source
Model-agnostic meta-learning framework for data loss detection with transfer learning
Intelligent Data AnalysisModel-Agnostic Meta-Learning (MAML) has proven to be effective in various learning environments. However, it faces challenges with domain adaptation because it depends on gradient-based optimization, which does not explicitly integrate prior knowledge ...
D. Naveenkumar, M. Karthikeyan
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Proceedings of the 11th ACM Workshop on Adaptive and Autonomous Cyber Defense
Network Intrusion Detection Systems (NIDS) are essential for identifying and mitigating cyber threats in dynamic network environments. However, maintaining high performance over time is challenging due to factors such as initial model limitations, data ...
Goktug Ekinci +4 more
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Network Intrusion Detection Systems (NIDS) are essential for identifying and mitigating cyber threats in dynamic network environments. However, maintaining high performance over time is challenging due to factors such as initial model limitations, data ...
Goktug Ekinci +4 more
semanticscholar +1 more source
Sparse Model-Agnostic Meta-Learning Algorithm for Few-Shot Learning
2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI), 2019Few-shot meta learning aims to obtain a prior from previous experiences, which is well used for new tasks during meta-test phase. The model-agnostic meta-learning (MAML) algorithm in [3] achieves this goal by finding a proper initial parameter at meta-training phase and this initialization could be quickly adapted to a new task by using the gradient ...
Sibo Gai, Donglin Wang
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Zero-shot classification of small target on sea bottom using model-agnostic meta-learning.
Journal of the Acoustical Society of AmericaA model-agnostic meta-learning (MAML)-based active target classifier to identify small targets (e.g., mines) on the sea bottom in different ocean environments from those present in the training data is proposed.
Heewon You, Yongmin Choo
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Hinglish Cross-Accent Model Agnostic Meta-Learning Automatic Speech Recognition
ACM Transactions on Asian and Low-Resource Language Information ProcessingMother tongues and regional dialects have a substantial impact on pronunciation, leading to a range of complex and unique accents. This complexity increases in a diverse country such as India, which has code-mixed languages, which necessitates the development of an Automatic Speech Recognition(ASR) system capable of accommodating these variations ...
Sanskar Singh +3 more
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