DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning
Learning robust representations for time-series signals under noise and distribution shifts remains challenging, especially in clinical applications such as electroencephalogram (EEG) and electrocardiogram (ECG) analysis. We propose Diffusion-Conditioned Representation Alignment (DCRA), a training framework that repurposes the forward diffusion process as a structured corruption scheduler for representation learning. Different from conventional augmentation and consistency-based methods that rely on independently sampled perturbations, DCRA introduces a structured corruption trajectory via the diffusion forward process, which enables continuous and controlled representation evolution acro...
| Authors | Wenrui Xu, Anas Enanaa, Keshab K. Parhi |
|---|---|
| Primary category | cs.LG |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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