过拟合,在AI领域多指机器学习得到模型太过复杂,导致在训练集上表现很好,然而在测试集上却不尽人意。过拟合(over-fitting)也称为过学习,它的直观表现是算法在训练集上表现好,但在测试集上表现不好,泛化性能差。过拟合是在模型参数拟合过程中由于训练数据包含抽样误差,在训练时复杂的模型将抽样误差也进行了拟合导致的。

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题目: Time Series Data Augmentation for Deep Learning: A Survey

摘要:

近年来,深度学习在许多时间序列分析任务中表现优异。深度神经网络的优越性能很大程度上依赖于大量的训练数据来避免过拟合。然而,许多实际时间序列应用的标记数据可能会受到限制,如医学时间序列的分类和AIOps中的异常检测。数据扩充是提高训练数据规模和质量的有效途径,是深度学习模型在时间序列数据上成功应用的关键。本文系统地综述了时间序列的各种数据扩充方法。我们为这些方法提出了一个分类,然后通过强调它们的优点和局限性为这些方法提供了一个结构化的审查。并对时间序列异常检测、分类和预测等不同任务的数据扩充方法进行了实证比较。最后,我们讨论并强调未来的研究方向,包括时频域的数据扩充、扩充组合、不平衡类的数据扩充与加权。

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Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processing despite many studies proposing various augmentation methods for image data. Actually, regularization is essential for point clouds since lack of generality is more likely to occur in point cloud due to small datasets. This paper proposes a Rigid Subset Mix (RSMix), a novel data augmentation method for point clouds that generates a virtual mixed sample by replacing part of the sample with shape-preserved subsets from another sample. RSMix preserves structural information of the point cloud sample by extracting subsets from each sample without deformation using a neighboring function. The neighboring function was carefully designed considering unique properties of point cloud, unordered structure and non-grid. Experiments verified that RSMix successfully regularized the deep neural networks with remarkable improvement for shape classification. We also analyzed various combinations of data augmentations including RSMix with single and multi-view evaluations, based on abundant ablation studies.

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Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processing despite many studies proposing various augmentation methods for image data. Actually, regularization is essential for point clouds since lack of generality is more likely to occur in point cloud due to small datasets. This paper proposes a Rigid Subset Mix (RSMix), a novel data augmentation method for point clouds that generates a virtual mixed sample by replacing part of the sample with shape-preserved subsets from another sample. RSMix preserves structural information of the point cloud sample by extracting subsets from each sample without deformation using a neighboring function. The neighboring function was carefully designed considering unique properties of point cloud, unordered structure and non-grid. Experiments verified that RSMix successfully regularized the deep neural networks with remarkable improvement for shape classification. We also analyzed various combinations of data augmentations including RSMix with single and multi-view evaluations, based on abundant ablation studies.

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