Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification

arXiv:2305.18671v2 Announce Type: replace-cross
Abstract: This paper introduces a novel Perturbation-Assisted Inference (PAI) framework utilizing synthetic data generated by the Perturbation-Assisted Sample Synthesis (PASS) method. The framework focuses on uncertainty quantification in complex data scenarios, particularly involving unstructured data while utilizing deep learning models. On one hand, PASS employs a generative model to create synthetic data that closely mirrors raw data while preserving its rank properties through data perturbation, thereby enhancing data diversity and bolstering privacy. By incorporating knowledge transfer …

abstract arxiv cs.lg data deep learning framework generated generative inference novel paper quantification sample stat.ml synthesis synthetic synthetic data type uncertainty unstructured unstructured data