论文标题
VNIBCREG：在非平稳地震信号时间序列上评估的邻近不变性和更好的搭配性
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time Series
论文作者
论文摘要
最新的自我监督学习（SSL）方法之一VICREG在线性评估和微调评估中表现出色。但是，在计算机视觉中提出了VICREG，它通过拉动图像的随机作物的表示，同时通过差异和协方差损失来维持表示空间来学习。但是，Vicreg在非平稳时间序列上将无效，在非平稳时间序列中，不同的输入零件/作物应具有不同的编码以考虑非平稳性。 SSL的另一个最新建议，暂时的邻里编码（TNC）对于编码非平稳时间序列有效。这项研究表明，Vicreg式方法和TNC的组合对于非平稳时间序列的SSL非常有效，在非平稳时间序列上，非平稳地震信号时间序列用作评估数据集。
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.
