Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition
Standard Self-Supervised Learning (SSL) for Automatic Modulation Recognition (AMR) struggles with ineffective isotropic augmentations, spectral instability, and semantic drift. To address these challenges, we propose Dynamic-Consistency Contrastive Learning (DyCo-CL), a geometry-aware framework that couples Virtual Adversarial Augmentation (VAA) with a semantic consistency loss. We provide a theoretical analysis indicating that this strategy acts as an implicit spectral regularizer for the encoder, enabling stable manifold exploration. Complementing this, our Signal-Adaptive Swin Backbone with fixed-window attention improves structural stability by constraining attention locality, while a...
| Authors | Guanqun Zhao, Yitong Liu, Jiaxuan Fang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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