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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Despite the recent success of speech separation models, they fail to separate sources properly while facing different sets of people or noisy environments. To tackle this problem, we proposed to apply meta-learning to the speech separation task. We aimed to find a meta-initialization model, which can quickly adapt to new speakers by seeing only one mixture generated by those people. In this paper, we use model-agnostic meta-learning(MAML) algorithm and almost no inner loop(ANIL) algorithm in Conv-TasNet to achieve this goal. The experiment results show that our model can adapt not only to a new set of speakers but also noisy environments. Furthermore, we found out that the encoder and decoder serve as the feature-reuse layers, while the separator is the task-specific module.

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Despite the recent success of speech separation models, they fail to separate sources properly while facing different sets of people or noisy environments. To tackle this problem, we proposed to apply meta-learning to the speech separation task. We aimed to find a meta-initialization model, which can quickly adapt to new speakers by seeing only one mixture generated by those people. In this paper, we use model-agnostic meta-learning(MAML) algorithm and almost no inner loop(ANIL) algorithm in Conv-TasNet to achieve this goal. The experiment results show that our model can adapt not only to a new set of speakers but also noisy environments. Furthermore, we found out that the encoder and decoder serve as the feature-reuse layers, while the separator is the task-specific module.

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