Semi-supervised time-series classification (TSC) faces challenges in handling intra-class variability and distribution shifts, which limit the effectiveness of standard contrastive learning methods. To address these limitations, we propose the Expert-Transformer with Prototype-Aware Contrastive Learning (ExT-PACL), a novel framework that integrates an uncertainty-guided Mixture-of-Experts (MoE) module within a Transformer encoder to dynamically capture diverse temporal patterns. An expert balancing strategy ensures all experts contribute meaningfully, preventing collapse and enhancing representation robustness. In addition, a prototype-aware contrastive learning loss guides both labeled and high-confidence unlabeled samples toward class prototypes, improving discriminative power and reducing reliance on large negative sample sets. Extensive experiments on multiple benchmark datasets demonstrate that ExT-PACL achieves superior generalization and state-of-the-art performance.
Huang et al. (Tue,) studied this question.