Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition
📰 ArXiv cs.AI
Learn how to apply geometry-aware contrastive learning for few-shot automatic modulation recognition using Dynamic-Consistency Contrastive Learning (DyCo-CL)
Action Steps
- Apply Virtual Adversarial Augmentation (VAA) to generate diverse and informative samples
- Implement a semantic consistency loss to maintain the semantic meaning of the samples
- Combine VAA with the semantic consistency loss using DyCo-CL
- Evaluate the performance of DyCo-CL on few-shot automatic modulation recognition tasks
- Compare the results with standard self-supervised learning methods
Who Needs to Know This
Researchers and engineers working on automatic modulation recognition and few-shot learning can benefit from this approach to improve the accuracy and robustness of their models
Key Insight
💡 Geometry-aware contrastive learning can improve the accuracy and robustness of few-shot automatic modulation recognition models
Share This
📡 Boost few-shot automatic modulation recognition with geometry-aware contrastive learning! 🚀
Full Article
Title: Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition
Abstract:
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 consistency loss. We provide a theoretical analysis indicating that this strategy acts
Abstract:
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 consistency loss. We provide a theoretical analysis indicating that this strategy acts
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