Multi-Initialization Meta-Learning with Domain Adaptation

Chen, Zhengyu; Wang, Donglin

doi:10.1109/icassp39728.2021.9414554

Cited by 17 publications

(6 citation statements)

References 7 publications

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“…Numerous jobs in the fields of industry, the military, medicine, and education demand constant attention with varying levels of cognitive workload. Security personnel [ 28 ], workers in charge of watching security cameras or baggage screening experts, operating vehicles, working in real classroom settings [ 29 ], as well as industrial and air traffic control [ 30 ], are examples that require high levels of attention. Vigilance is necessary for these tasks to be completed with sufficient cognitive efficiency; however, assessing vigilance is a considerable problem.…”

Section: Related Workmentioning

confidence: 99%

Assessment of Vigilance Level during Work: Fitting a Hidden Markov Model to Heart Rate Variability

et al. 2023

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This study aimed to enhance the real-time performance and accuracy of vigilance assessment by developing a hidden Markov model (HMM). Electrocardiogram (ECG) signals were collected and processed to remove noise and baseline drift. A group of 20 volunteers participated in the study. Their heart rate variability (HRV) was measured to train parameters of the modified hidden Markov model for a vigilance assessment. The data were collected to train the model using the Baum–Welch algorithm and to obtain the state transition probability matrix A^ and the observation probability matrix B^. Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and the Viterbi algorithm was used to find the optimal state, which was compared with the actual state. The constructed vigilance assessment model had a high accuracy rate, and the accuracy rate of data prediction for these three volunteers exceeded 80%. Our approach can be used in wearable products to improve their vigilance level assessment functionality or in other fields that have key positions with high concentration requirements and monotonous repetitive work.

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Section: Related Workmentioning

confidence: 99%

Assessment of Vigilance Level during Work: Fitting a Hidden Markov Model to Heart Rate Variability

et al. 2023

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“…Numerous jobs in the fields of industry, the military, medicine, and education demand constant attention with varied levels of cognitive workload. Security personnel [22], workers in charge of watching security cameras or baggage screening experts, operating vehicles, real classroom settings [23], as well as industrial and air traffic control [24], are examples of applications that require ongoing attention. For these tasks to be completed with a sufficient level of cognitive efficiency, a certain range of arousal is required.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Vigilance Assessment using Hidden Markov Model

Wang¹,

Chen²,

Qiao³

et al. 2022

Preprint

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Purpose: To construct a hidden Markov model (HMM) for vigilance assessment to improve the real-time performance and accuracy of current vigilance measurement. Methods: ECG signal was collected by sensors, while the noise and baseline drift was eliminated from the original ECG signal. 10 volunteers were randomly selected. Their heart rate variability (HRV) were measured and trained parameters of the modified Hidden Markov model for vigilance assessment. Then, these data were collected to optimize using the Baum-Welch algorithm and obtained the state transition probability matrix A ̂ and the observation probability matrix B ̂. Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and used the Viterbi algorithm to find the optimal state, which compared with the actual state. Results: The constructed vigilance assessment model had a high accuracy rate the accuracy rate of data prediction for these three volunteers exceeded 80%. Conclusion: The Hidden Markov model for vigilance assessment can accurately predict the vigilance level and indicate broad application prospects.

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“…Current meta-learning approaches for the few-shot problem can be roughly divided into three groups: optimization-based, model-based, and metric-based. Optimization-based approaches [5,6,13] learn a meta-learner to adjust the optimization algorithm, usually by providing better initialization or search steps for parameters. Model-based [1,17,19,30] approaches depend on well-designed models, whose parameters are obtained with its internal architecture or a meta-learner for fast learning.…”

Section: Few-shot Learningmentioning

confidence: 99%

Pareto Self-Supervised Training for Few-Shot Learning

Chen¹,

Ge²,

Zhan³

et al. 2021

Preprint

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While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data. Exploiting the complementarity of these two manners, few-shot auxiliary learning has recently drawn much attention to deal with few labeled data. Previous works benefit from sharing inductive bias between the main task (FSL) and auxiliary tasks (SSL), where the shared parameters of tasks are optimized by minimizing a linear combination of task losses. However, it is challenging to select a proper weight to balance tasks and reduce task conflict. To handle the problem as a whole, we propose a novel approach named as Pareto self-supervised training (PSST) for FSL. PSST explicitly decomposes the few-shot auxiliary problem into multiple constrained multi-objective subproblems with different trade-off preferences, and here a preference region in which the main task achieves the best performance is identified. Then, an effective preferred Pareto exploration is proposed to find a set of optimal solutions in such a preference region. Extensive experiments on several public benchmark datasets validate the effectiveness of our approach by achieving state-of-the-art performance.

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Multi-Initialization Meta-Learning with Domain Adaptation

Cited by 17 publications

References 7 publications

Assessment of Vigilance Level during Work: Fitting a Hidden Markov Model to Heart Rate Variability

Assessment of Vigilance Level during Work: Fitting a Hidden Markov Model to Heart Rate Variability

An Approach for Vigilance Assessment using Hidden Markov Model

Pareto Self-Supervised Training for Few-Shot Learning

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