ProtoSSL: Interpretable Prototype Learning from Unlabeled Time-Series Data
For practitioners needing interpretable models in time-series domains (e.g., healthcare), ProtoSSL reduces reliance on large labeled datasets while maintaining or improving performance and interpretability.
ProtoSSL introduces a self-supervised framework to learn interpretable, projection-based prototypes from unlabeled time-series data, which can then be adapted to downstream tasks. It outperforms supervised prototype baselines in low-data regimes (e.g., 256 labeled examples) and at full scale on ECG datasets, and produces prototypes judged more favorably in human evaluation.
In time-series domains where both predictive performance and interpretability are essential, deep neural networks achieve strong results but provide limited insight into how their predictions are made. Projection-based prototype networks address this limitation by grounding predictions in similarity to representative training examples, enabling case-based explanations and global prototype inspection. However, existing approaches rely on label supervision, tying prototypes to a specific task and requiring large labeled datasets. We introduce ProtoSSL, a novel framework for learning interpretable, projection-based prototypes from unlabeled time-series data and adapting them to downstream tasks. Our key idea is to separate motif discovery from label alignment. ProtoSSL first learns a reusable prototype bank using a self-supervised objective applied directly to prototype activations, and then aligns these prototypes to downstream tasks through an efficient assignment procedure. Across six electrocardiography (ECG) datasets, ProtoSSL improves label efficiency, outperforming supervised prototype baselines in low-data regimes with as few as 256 labeled examples; with fine-tuning, ProtoSSL outperforms supervised prototype baselines at full dataset scale. In a human evaluation study, ProtoSSL produces prototypes and prototype-based explanations that are judged more favorably than those learned with direct label supervision. We further show that the framework extends to audio classification. Thus, ProtoSSL enables both learning generalizable prototypes from unlabeled data before the downstream label space is known, and subsequent assignment of interpretable, projection-grounded prototypes to new time-series tasks.