Class Conditional Time Series Generation with Structured Noise Space GAN
This incremental work addresses class-conditional generation for time series and image data, potentially benefiting domains like data augmentation or simulation.
The paper tackled the challenge of integrating class labels into generative models without network modifications by introducing Structured Noise Space GAN (SNS-GAN), which embeds conditions in the noise space, resulting in simplified training and superior performance in time series generation compared to baselines.
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.