Time-Series Contrastive Learning against False Negatives and Class
  Imbalance
Xiyuan Jin Jing Wang Lei Liu Youfang Lin
As an exemplary self-supervised approach for representation learning,
time-series contrastive learning has exhibited remarkable advancements in
contemporary research. While recent contrastive learning strategies have
focused on how to construct appropriate positives and negatives, in this study,
we conduct theoretical analysis and find they have overlooked the fundamental
issues: false negatives and class imbalance inherent in the InfoNCE loss-based
framework. Therefore, we introduce a straightforward modification grounded in
the SimCLR framework, universally adaptable to models engaged in the instance
discrimination task. By constructing instance graphs to facilitate interactive
learning among instances, we emulate supervised contrastive learning via the
multiple-instances discrimination task, mitigating the harmful impact of false
negatives. Moreover, leveraging the graph structure and few-labeled data, we
perform semi-supervised consistency classification and enhance the
representative ability of minority classes. We compared our method with the
most popular time-series contrastive learning methods on four real-world
time-series datasets and demonstrated our significant advantages in overall
performance.