度量学习的目的为了衡量样本之间的相近程度,而这也正是模式识别的核心问题之一。大量的机器学习方法,比如K近邻、支持向量机、径向基函数网络等分类方法以及K-means聚类方法,还有一些基于图的方法,其性能好坏都主要有样本之间的相似度量方法的选择决定。 度量学习通常的目标是使同类样本之间的距离尽可能缩小,不同类样本之间的距离尽可能放大。

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题目: Continual Learning of Object Instances

摘要: 我们建议实例持续学习——一种将持续学习的概念应用于区分相同对象类别的实例的任务的方法。我们特别关注car对象,并通过度量学习逐步学会区分car实例与其他实例。我们从评估当前的技术开始我们的论文。在现有的方法中,灾难性遗忘是显而易见的,我们提出了两个补救措施。首先,通过归一化交叉熵对度量学习进行正则化。其次,我们使用合成数据传输来扩充现有的模型。我们在三个大型数据集上进行了大量的实验,使用了两种不同的体系结构,采用了五种不同的持续学习方法,结果表明,标准化的交叉熵和合成转移可以减少现有技术中的遗忘。

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Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical features extracted from the successive word sequence may introduce some noise that has little or no meaningful content. Meanwhile, the syntactic features are usually encoded via graph convolutional networks which have restricted receptive field. To address the above limitations, we propose a multi-scale feature and metric learning framework for relation extraction. Specifically, we first develop a multi-scale convolutional neural network to aggregate the non-successive mainstays in the lexical sequence. We also design a multi-scale graph convolutional network which can increase the receptive field towards specific syntactic roles. Moreover, we present a multi-scale metric learning paradigm to exploit both the feature-level relation between lexical and syntactic features and the sample-level relation between instances with the same or different classes. We conduct extensive experiments on three real world datasets for various types of relation extraction tasks. The results demonstrate that our model significantly outperforms the state-of-the-art approaches.

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Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical features extracted from the successive word sequence may introduce some noise that has little or no meaningful content. Meanwhile, the syntactic features are usually encoded via graph convolutional networks which have restricted receptive field. To address the above limitations, we propose a multi-scale feature and metric learning framework for relation extraction. Specifically, we first develop a multi-scale convolutional neural network to aggregate the non-successive mainstays in the lexical sequence. We also design a multi-scale graph convolutional network which can increase the receptive field towards specific syntactic roles. Moreover, we present a multi-scale metric learning paradigm to exploit both the feature-level relation between lexical and syntactic features and the sample-level relation between instances with the same or different classes. We conduct extensive experiments on three real world datasets for various types of relation extraction tasks. The results demonstrate that our model significantly outperforms the state-of-the-art approaches.

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