Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI

arXiv:2404.07377v1 Announce Type: new
Abstract: Building on the remarkable achievements in generative sampling of natural images, we propose an innovative challenge, potentially overly ambitious, which involves generating samples of entire multivariate time series that resemble images. However, the statistical challenge lies in the small sample size, sometimes consisting of a few hundred subjects. This issue is especially problematic for deep generative models that follow the conventional approach of generating samples from a canonical distribution and then decoding or denoising them …

abstract arxiv building challenge cs.ai cs.cl cs.cv cs.it cs.lg data divergence generative however images lies math.it multivariate natural resemble samples sampling series small space statistical time series type