The ability to generate synthetic sequences is crucial for a wide range of applications, and recent advances in deep learning architectures and generative frameworks have greatly facilitated this process. Particularly, unconditional one-shot generative models constitute an attractive line of research that focuses on capturing the internal information of a single image or video to generate samples with similar contents. Since many of those one-shot models are shifting toward efficient non-deep and non-adversarial approaches, we examine the versatility of a one-shot generative model for augmenting whole datasets. In this work, we focus on how similarity at the subsequence level affects similarity at the sequence level, and derive bounds on the optimal transport of real and generated sequences based on that of corresponding subsequences. We use a one-shot generative model to sample from the vicinity of individual sequences and generate subsequence-similar ones and demonstrate the improvement of this approach by applying it to the problem of Unmanned Aerial Vehicle (UAV) identification using limited radio-frequency (RF) signals. In the context of UAV identification, RF fingerprinting is an effective method for distinguishing legitimate devices from malicious ones, but heterogenous environments and channel impairments can impose data scarcity and affect the performance of classification models. By using subsequence similarity to augment sequences of RF data with a low ratio (5%-20%) of training dataset, we achieve significant improvements in performance metrics such as accuracy, precision, recall, and F1 score.

翻译：合成序列的生成能力对广泛应用至关重要，而深度学习架构与生成框架的最新进展极大促进了这一过程。特别是，无条件的单样本生成模型代表了一条引人关注的研究方向，其专注于捕捉单张图像或视频的内部信息以生成内容相似的样本。由于许多单样本模型正转向高效的非深度与非对抗性方法，我们检验了单样本生成模型在增强整个数据集时的通用性。本文重点研究子序列层面的相似性如何影响序列层面的相似性，并基于对应子序列的最优传输推导出真实序列与生成序列之间的界限。我们利用单样本生成模型从单个序列的邻域采样，生成与之子序列相似的序列，并通过将其应用于使用有限射频信号进行无人机（UAV）识别的问题，展示了该方法的改进效果。在无人机识别领域，射频指纹识别是区分合法设备与恶意设备的有效方法，但异构环境和信道损伤可能导致数据稀缺，影响分类模型的性能。通过利用子序列相似性以低比率（5%-20%）的训练数据集增强射频数据序列，我们在准确率、精确率、召回率和F1分数等性能指标上取得了显著提升。