# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 2 Related Work
### Open-Set Recognition  
OSR is to identify the known class samples seen during the training phase and reject the unknown class samples not seen in the training phase, i.e., to achieve high unknown class performance rejection and to keep excellent known classes recognition performances. Therefore, it is necessary to design a special model for the OSR problem [24; 25; 26; 27; 28; 29; 30]. Open-Max [24] used Weibull-based calibration to augment the Softmax layer of a deep classifier, but its OSR performance is poor. A competitive overcompleted output layer (COOL) neural network [25] was proposed to avoid overgeneralization over regions far from the training data. OSRCI [26] used GAN to generate \"fake\" unknown class samples, which are closed to known class samples but do not belong to a known class, to assist in network training. In Refs. [27; 28], the OSR performance of the network was further enhanced by modifying the model based on OSRCI. The authors in [29] constructed faithful unknown class samples based on a generative causal model, decoupled the internal relationship between sample features and category features by designing a special loss function and obtained a better image OSR performance. OpenGAN [30] obtained outlier data according to outlier exposure [31] and selected the discrimination model with the optimal performance by using outlier data, which solved the problem of unstable training of GAN in the OSR. Although, to some extent, combining GAN can improve the OSR ability in image classification, the quality of \"fake\" interference signals with additive noise generated by GAN is not high, so it even reduces the OSR performance for interference signals.  
The above works are all advanced research for image open-set recognition. For open-set recognition of wireless signals, some works have also been carried out [1; 16; 17; 18; 19]. The authors in [16] used a classification task and signal reconstruction task to jointly train the feature extractor and used another Siamese network to realize the OSR of radio frequency fingerprinting. However, multiple networks make the process very complicated. Conditional VAE was used to generate \"fake\" unknown classes signal in [17], while boundary known class signals were used to imitate unknown class signals in [18], but their performance depends on the quality of \"fake\" unknown class signals. Multi-task learning was used to get better feature representation, and CountGAN was used to generate \"fake\" unknown classes according to known boundary class signals in [19], but it needs more priori information to carry out multi-task learning, and the quality of the generated signal is still an important role affecting the performance. Combined with VAE and CGDL [32], CG-Encoder was proposed in [1] to realize the OSR of interference signals. It has an excellent unknown class rejection performance under a high jamming-noiseratio (JNR), but its anti-noise ability is poor, i.e., its performance decreases significantly under a low JNR. The proposed method in this paper can achieve a high OSR performance of interference signals even under a low JNR.