Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition 事件

Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition arXiv:2605.26600v1 Announce Type: cross Abstract: 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 con