The success of self-supervised contrastive learning hinges on identifying positive data pairs that, when pushed together in embedding space, encode useful information for subsequent downstream tasks. However, in time-series, this is challenging because creating positive pairs via augmentations may break the original semantic meaning. We hypothesize that if we can retrieve information from one subsequence to successfully reconstruct another subsequence, then they should form a positive pair. Harnessing this intuition, we introduce our novel approach: REtrieval-BAsed Reconstruction (REBAR) contrastive learning. First, we utilize a convolutional cross-attention architecture to calculate the REBAR error between two different time-series. Then, through validation experiments, we show that the REBAR error is a predictor of mutual class membership, justifying its usage as a positive/negative labeler. Finally, once integrated into a contrastive learning framework, our REBAR method can learn an embedding that achieves state-of-the-art performance on downstream tasks across various modalities.

翻译：自监督对比学习的成功关键在于识别正数据对，当它们在嵌入空间中被拉近时，能够为后续下游任务编码有用信息。然而，在时间序列中，这一过程具有挑战性，因为通过数据增强创建正数据对可能会破坏原有的语义含义。我们假设，如果能够从一个子序列中检索信息以成功重建另一个子序列，那么它们应构成正数据对。基于这一直觉，我们提出了一种创新方法：基于检索重建的对比学习（REBAR）。首先，我们利用卷积交叉注意力架构计算两个不同时间序列之间的REBAR误差。然后，通过验证实验表明，REBAR误差能够预测互类隶属关系，从而证明其作为正/负标签器的合理性。最后，一旦集成到对比学习框架中，我们的REBAR方法能够学习到一种嵌入表示，该表示在多种模态的下游任务上均实现了最先进的性能。