This paper introduces Structured Noise Space GAN (SNS-GAN), a novel approach in the field of generative modeling specifically tailored for class-conditional generation in both image and time series data. It addresses the challenge of effectively integrating class labels into generative models without requiring structural modifications to the network. The SNS-GAN method embeds class conditions within the generator's noise space, simplifying the training process and enhancing model versatility. The model's efficacy is demonstrated through qualitative validations in the image domain and superior performance in time series generation compared to baseline models. This research opens new avenues for the application of GANs in various domains, including but not limited to time series and image data generation.

结构噪声空间生成对抗网络（SNS-GAN）是一种针对类别条件生成在图像和时间序列数据中的生成建模领域的新方法，它解决了有效地将类别标签集成到生成模型中而不需要对网络进行结构修改的挑战，该方法将类别条件嵌入到生成器的噪声空间中，简化了训练过程并增强了模型的多功能性，通过图像领域的定性验证以及相对于基准模型在时间序列生成任务中的卓越性能，证明了该模型的有效性，此研究为GAN在时间序列和图像数据生成等不同领域的应用开辟了新的道路。

具有结构噪声空间生成的类条件时间序列