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.

通过检索 - 重建（REBAR）对比学习，我们提出了一种利用卷积交叉注意力体系结构计算两个不同时间序列之间的 REBAR 误差的新方法，并通过验证实验表明，REBAR 误差是相互类别成员资格的预测器，从而为其用作正 / 负标签赋予了合理性。最后，一旦集成到对比学习框架中，我们的 REBAR 方法可以学习到在各种模态下取得最先进性能的嵌入。