DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning

2026-09-09

AI Summary

为解决时间序列信号在噪声和分布偏移下的鲁棒表示学习问题，提出DCRA方法，通过扩散过程引入结构化破坏轨迹，并在特征层面保持一致性。

Abstract

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 across noise levels. We introduce a feature-level consistency objective that aligns representations across noise levels while preserving class-discriminative structure. This mechanism promotes structure-preserving consistency, which enables smooth and semantically coherent feature trajectories in latent space. The proposed framework is encoder-agnostic and can be integrated with state space models and Transformer architectures. The seizure detection experiments on the CHB-MIT EEG dataset show that DCRA consistently improves performance under multiple noise conditions and achieves higher sensitivity at low false-positive rates. Analysis reveals that DCRA produces more balanced and structured representations compared to baseline and diffusion-only models. These findings highlight the benefit of combining structured corruption with representation alignment for robust time-series learning.

Problem

• time-series signals
• noise and distribution shifts
• clinical applications
• representation learning
• robust

Innovation

• Diffusion-Conditioned Representation Alignment
• structured corruption scheduler
• feature-level consistency objective
• time-series learning
• encoder-agnostic