Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition

• URL: http://arxiv.org/abs/2311.08179v1
• Date: Tue, 14 Nov 2023 14:08:55 GMT
• Title: Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition
• Authors: Weidong Wang, Hongshu Liao, and Lu Gan
• Abstract summary: Training a deep neural network on small datasets with few labels generally falls into overfitting, resulting in degenerated performance. We develop a semi-supervised learning (SSL) method that effectively utilizes a large collection of more readily available unlabeled signal data to improve generalization.
• Score: 11.325643693823828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have been widely used in communication signal recognition and achieved remarkable performance, but this superiority typically depends on using massive examples for supervised learning, whereas training a deep neural network on small datasets with few labels generally falls into overfitting, resulting in degenerated performance. To this end, we develop a semi-supervised learning (SSL) method that effectively utilizes a large collection of more readily available unlabeled signal data to improve generalization. The proposed method relies largely on a novel implementation of consistency-based regularization, termed Swapped Prediction, which leverages strong data augmentation to perturb an unlabeled sample and then encourage its corresponding model prediction to be close to its original, optimized with a scaled cross-entropy loss with swapped symmetry. Extensive experiments indicate that our proposed method can achieve a promising result for deep SSL of communication signal recognition.

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