DGSIN: Discrepancy-Gated Source–Instance Normalization for Cross-Domain Time-Series Semantic Segmentation

Cross-domain time-series semantic segmentation aims to label temporal segments in unseen domains. Representative approaches use feature alignment or statistical normalization to reduce feature discrepancies across domains. However, these discrepancies can reflect both domain factors and semantic variation, suppressing them may also weaken the features that help distinguish semantic states. Normalization methods in visual recognition learn a trade-off between batch and instance normalization, but their input-independent mixing coefficients do not explicitly respond to changes between temporal segments.We propose discrepancy-gated source–instance normalization (DGSIN), which learns segment-dependent trade-offs through the segmentation objective. DGSIN uses source–instance statistical discrepancies to generate layer–channel coefficients that control the correction from source-normalized to instance-normalized representations. At inference, a shared network averages prediction probabilities under different frozen source statistics. Across five leave-one-dataset-out sleep-staging tasks, DGSIN achieves 72.43% mean macro-F1, 74.30% balanced accuracy, and 0.688 Cohen’s κ. Its mean macro-F1 exceeds DSON’s 72.14% and PSDNorm’s 71.47%, with the highest macro-F1 on four targets. DGSIN provides a concrete approach to learning segment-dependent source–instance normalization trade-offs for cross-domain temporal segmentation.Source code is available at https://anonymous.4open.science/r/Dgsin-E9DB.