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.

翻译：本文提出结构化噪声空间生成对抗网络（SNS-GAN），这是一种专门针对图像和时间序列数据中类条件生成任务的新型生成建模方法。该方法解决了在不需对网络进行结构性修改的前提下，如何有效将类别标签整合到生成模型中的关键挑战。SNS-GAN通过在生成器的噪声空间中嵌入类条件信息，简化了训练过程并增强了模型的通用性。通过图像领域的定性验证以及与基线模型相比在时间序列生成任务中的卓越表现，充分证明了该模型的有效性。本研究为生成对抗网络在包括时间序列和图像数据生成在内的多领域应用开辟了新途径。