K-ICL: Kernel-based Pair Mining for Instance-wise Contrastive Learning of Time Series

Contrastive Learning (CL) is a Self-Supervised Learning (SSL) paradigm that learns representations by attracting *positive* pairs and repelling *negative* ones. SimCLR, a cornerstone CL method, forms positives through label-preserving augmentations of the same instance, and negatives from augmented views of different samples. This instance-wise approach is a key ingredient of many CL solutions, especially in computer vision, but suffers of a major limitation: it is affected by *ill-defined negatives* generated from instances sharing the same semantics. A second limitation specifically concerns time-series, where augmentation can alter the semantic of the instance, producing *ill-defined positives*, which has so far hindered SimCLR in the time-series domain. We address both limitations with K-ICL, a novel probabilistic pair-mining strategy for instance-wise CL that leverages a kernel to measure similarity between time series directly in the input space. Such similarity steers the sampling process towards *well-defined* positive and negative pairs, using only weak augmentations suitable for time-series. Experiments on time-series identification and classification show that K-ICL consistently outperforms SimCLR and its instance-wise extensions, in both few-class and unbalanced settings.