Deep Contrastive Representation Learning With Self-Distillation

Recently, contrastive learning (CL) is a promising way of learning discriminative representations from time series data. In the representation hierarchy, semantic information extracted from lower levels is the basis of that captured from higher levels. Low-level semantic information is essential and should be considered in the CL process. However, the existing CL algorithms mainly focus on the similarity of high-level semantic information. Considering the similarity of low-level semantic information may improve the performance of CL. To this end, we present a deep contrastive representation learning with self-distillation (DCRLS) for the time series domain. DCRLS gracefully combine data augmentation, deep contrastive learning, and self distillation. Our data augmentation provides different views from the same sample as the input of DCRLS. Unlike most CL algorithms that concentrate on high-level semantic information only, our deep contrastive learning also considers the contrast similarity of low-level semantic information between peer residual blocks. Our self distillation promotes knowledge flow from high-level to low-level blocks to help regularize DCRLS in the knowledge transfer process. The experimental results demonstrate that the DCRLS-based structures achieve excellent performance on classification and clustering on 36 UCR2018 datasets.