Class Conditional Time Series Generation with Structured Noise Space GAN
Hamidreza Gholamrezaei Alireza Koochali Andreas Dengel Sheraz Ahmed
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.