Due to the emergence of deep learning, signal recognition has made great strides in performance improvement. The success of most deep learning methods relies on the accessibility of abundant labeled training data. However, the annotation of signals is quite expensive, making it challenging to train deep learning models substantially. This calls for the development of semi-supervised learning (SSL) method to fully utilize the unlabeled data to assist the training of deep learning models. To achieve this goal, three types of loss functions, tailored to the task of SLL-based signal recognition, are carefully designed in this paper. Together with a carefully selected neural network structure, the proposed SSL method can effectively extract the information from unlabeled training data and thus overcome the difficulty of insufficient training. Extensive numerical results using open source datasets are presented to show the superior performance of the proposed SSL method.

中文翻译：

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SSRCNN：信号识别的半监督学习框架

由于深度学习的出现，信号识别在性能提升方面取得了长足的进步。大多数深度学习方法的成功依赖于丰富的标记训练数据的可访问性。然而，信号的注释非常昂贵，这使得大量训练深度学习模型具有挑战性。这就需要开发半监督学习（SSL）方法来充分利用未标记的数据来辅助深度学习模型的训练。为了实现这一目标，本文精心设计了三种类型的损失函数，专门针对基于 SLL 的信号识别任务。结合精心选择的神经网络结构，所提出的 SSL 方法可以有效地从未标记的训练数据中提取信息，从而克服训练不足的困难。