MAML(Model-Agnostic Meta-Learning)是元学习(Meta learning)最经典的几个算法之一,出自论文《Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks》。 原文地址:https://arxiv.org/abs/1703.03400

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图分类的目的是对图结构数据进行准确的信息提取和分类。在过去的几年里,图神经网络(GNNs)在图分类任务上取得了令人满意的成绩。然而,大多数基于GNNs的方法侧重于设计图卷积操作和图池操作,忽略了收集或标记图结构数据比基于网格的数据更困难。我们利用元学习来进行小样本图分类,以减少训练新任务时标记图样本的不足。更具体地说,为了促进图分类任务的学习,我们利用GNNs作为图嵌入主干,利用元学习作为训练范式,在图分类任务中快速捕获特定任务的知识并将其转移到新的任务中。为了提高元学习器的鲁棒性,我们设计了一种新的基于强化学习的步进控制器。实验表明,与基线相比,我们的框架运行良好。

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As 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 time-consuming to collect enough data samples for each fault category, which brings challenges to training a robust classifier. This paper proposes a few-shot learning framework for bearing anomaly detection based on model-agnostic meta-learning (MAML), which aims to train an effective fault classifier using very limited data. In addition, it can use training data and learn to more effectively identify new fault conditions. A case study on the generalization of new artificial faults shows that this method can achieve up to 25\% overall accuracy when compared to a benchmark study based on the Siamese network. Finally, the generalization ability of MAML is also competitive when compared with some state-of-the-art few-shot learning methods in terms of identifying realistic bearing damages using a sufficient amount of training data from artificial damages.

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As 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 time-consuming to collect enough data samples for each fault category, which brings challenges to training a robust classifier. This paper proposes a few-shot learning framework for bearing anomaly detection based on model-agnostic meta-learning (MAML), which aims to train an effective fault classifier using very limited data. In addition, it can use training data and learn to more effectively identify new fault conditions. A case study on the generalization of new artificial faults shows that this method can achieve up to 25\% overall accuracy when compared to a benchmark study based on the Siamese network. Finally, the generalization ability of MAML is also competitive when compared with some state-of-the-art few-shot learning methods in terms of identifying realistic bearing damages using a sufficient amount of training data from artificial damages.

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