mlsanityData to 7 Oct 2026

Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification

arXiv 2408.05151 ↗First posted 9 Aug 2024cs.LG cs.AI eess.SPcited by 4

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Abstract (arXiv)

Automatic modulation classification (AMC) is an effective way to deal with physical layer threats of the internet of things (IoT). However, there is often label mislabeling in practice, which significantly impacts the performance and robustness of deep neural networks (DNNs). In this paper, we propose a meta-learning guided label noise distillation method for robust AMC. Specifically, a teacher-student heterogeneous network (TSHN) framework is proposed to distill and reuse label noise. Based on the idea that labels are representations, the teacher network with trusted meta-learning divides and conquers untrusted label samples and then guides the student network to learn better by reassessing and correcting labels. Furthermore, we propose a multi-view signal (MVS) method to further improve the performance of hard-to-classify categories with few-shot trusted label samples. Extensive experimental results show that our methods can significantly improve the performance and robustness of signal AMC in various and complex label noise scenarios, which is crucial for securing IoT applications.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 60 references that are in the map. First posted on arXiv 9 Aug 2024.

Builds on

21 of 60 references.

References · 21

  1. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  2. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
  3. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 994
  4. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  5. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  6. 2017Robust Loss Functions under Label Noise for Deep Neural Networkscited by 441
  7. 2018Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noisecited by 216
  8. 2017Over the Air Deep Learning Based Radio Signal Classificationcited by 121
  9. 2016Convolutional Radio Modulation Recognition Networkscited by 106
  10. 2017Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensorscited by 44
  11. 2017Deep Architectures for Modulation Recognitioncited by 33
  12. 2022On Learning Contrastive Representations for Learning with Noisy Labelscited by 21
9 more
  1. 2021Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrectioncited by 20
  2. 2022Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challengescited by 16
  3. 2020SR2CNN: Zero-Shot Learning for Signal Recognitioncited by 13
  4. 2014Image processingcited by 11
  5. 2023Mitigating Label Noise through Data Ambiguationcited by 10
  6. 2023Regularly Truncated M-estimators for Learning with Noisy Labelscited by 8
  7. 2021Challenges and Opportunities in Securing the Industrial Internet of Thingscited by 4
  8. 2024A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classificationcited by 3
  9. 2022Model and Data Agreement for Learning with Noisy Labelscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classificationcited by 2
  2. 2025Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classificationscited by 2
  3. 2025Adapt under Attack and Domain Shift: Unified Adversarial Meta-Learning and Domain Adaptation for Robust Automatic Modulation Classificationcited by 2
  4. 2026Transformer-Based Cognitive Radio: Adaptive Modulation Strategies Using Transformer Modelscited by 0