## 5 Discussion  
In this paper, we present an efficient data augmentation method for class imbalance modulation recognition in the low-data regime. In particular, we present four new and one combination of label-preserving transformations as well as an efficient automatic data augmentation strategy that does not require a separate search for data augmentation policies. To validate their effectiveness, the proposed methods are thoroughly evaluated and compared using multiple types of datasets and three representative SOTA deep neural network models. Particularly in low-data, imbalanced-class regimes, the proposed method can significantly improve the recognition performance of deep neural network models.  
There are still some intriguing unanswered questions. Despite the fact that we provide two methods for obtaining the final augmented sequence, there is no guarantee that the combination of augmentations obtained by these two methods is optimal. Is it then possible to use a multi-stage selection approach, such as selecting the more helpful transformations first, such as Rot and SP, and then randomly selecting other transformations with smaller contributions? Furthermore, questions such as how the individual augmentation methods in the SigAugment transform set interact with one another and whether some of these combinations cancel each other out need to be thoroughly investigated.  
All of the transformations used in SigAugment are label-preserving. Label interpolation generation methods, such as mixup [20], have been shown to be more effective than label-preserving methods in a variety of tasks in the vision domain. So, can label interpolation methods be extended to raw IQ or AP data in the communications signal domain? This could be the next area to take a gander at. However, based on our preliminary results, direct interpolation of samples and labels for mixing does not work. However, mixing samples from the same category while preserving the labels is worth a shot.  
Based on the results in Table 5, we chose the best-performing class rebalancing method from each of the three baseline models on the 2016A-50 dataset and added these methods and their corresponding models to the SigAugment method for joint training, as shown in Table 8. We can see that this combined approach does not result in improved performance but rather lower recognition accuracy. It is an intriguing phenomenon that the two methods used in the combination both improve the model's performance, but the combination fails. In order to find a solution, it may be worthwhile to examine the mechanism of the combination in greater detail in future work.  
Furthermore, when we compare Figure 8 to Table 6, we see that the three best methods for individual transformations are Rot, SP, and FCS, whereas for automatic data augmentation methods, we evaluate the gain from a specific transformation by adding it to the transformation pool of SigAugment; when we use the same approach for individual transformations, the top three methods are SP, Inv, and FCS, which does not appear to be the expected result. This also highlights the fact that the relationship between the effectiveness of individual data augmentation methods and their combined overall effect is complicated. As a result, the expansion of the SigAugment transformation pool appears to be more open and full of possibilities, and augmentation techniques that do not work as well when applied independently might achieve notable benefits in SigAugment.