One of the latest self-supervised learning (SSL) methods, VICReg, showed a great performance both in the linear evaluation and the fine-tuning evaluation. However, VICReg is proposed in computer vision and it learns by pulling representations of random crops of an image while maintaining the representation space by the variance and covariance loss. However, VICReg would be ineffective on non-stationary time series where different parts/crops of input should be differently encoded to consider the non-stationarity. Another recent SSL proposal, Temporal Neighborhood Coding (TNC) is effective for encoding non-stationary time series. This study shows that a combination of a VICReg-style method and TNC is very effective for SSL on non-stationary time series, where a non-stationary seismic signal time series is used as an evaluation dataset.
翻译:最新的自监督学习方法之一,即国际中心区域中心,在线性评价和微调评价方面表现良好,然而,国际中心区域中心是在计算机视野中提议的,它通过拉动图像随机作物的表示方式,同时保持差异和共变损失的表示空间来学习的。然而,国际中心区域中心在非静止时间序列上是无效的,因为对输入的不同部分/分节应进行不同的编码,以考虑非静态性。另一个最近的建议,即时间邻里编码对编码非静止时间序列是有效的。这项研究表明,在非静止时间序列上,将国际中心区域模式方法和跨国公司结合起来对空间中心非常有效,因为非静止地震信号时间序列被用作评价数据集。