ZSCIL: Zero-Shot Class Incremental Learning Method for Signal Recognition

A significant challenge in signal recognition tasks is identifying classes not present in the dataset. Zero-shot learning-based signal recognition addresses the challenge by identifying previously unseen classes in a mixed signal space without supervision. However, most existing methodologies are limited to one-time recognition processes. We propose a zero-shot class incremental learning (ZSCIL) method to achieve continuous unseen classes identification. Our model employs an encoder-decoder architecture and incorporates a triplet loss function to train the classifier, thereby enhancing the model's ability to recognize mixed signals through a metric learning paradigm. Additionally, we utilize class incremental learning, where the identified unseen signals are stored in a fixed-size buffer with a maximum diversity data replay mechanism. These signals are then used for incremental training. The framework's effectiveness and generality of our method are demonstrated through a series of experiments on two datasets. For instance, we achieved a significant 14.4% accuracy improvement for seen classes and that of the unseen classes by 4.2% on the DeepSig 2016.04C dataset. To the best of our knowledge, ZSCIL is the first method to implement sustainable identification for unseen classes in the mixed signal space.