Supplemental Material: Lifelong Generative Modelling Using Dynamic Expansion Graph Model

TL;DR

This paper provides supplementary material for a lifelong generative modeling approach using a dynamic expansion graph model, including visual results, numerical data, and detailed proofs to support the theoretical framework.

Contribution

It introduces a dynamic expansion graph model for lifelong generative learning, with comprehensive proofs and results to validate its effectiveness.

Findings

Enhanced visual and numerical results on challenging datasets

Theoretical analysis supporting the model's validity

Open-source code available for reproducibility

Abstract

In this article, we provide the appendix for Lifelong Generative Modelling Using Dynamic Expansion Graph Model. This appendix includes additional visual results as well as the numerical results on the challenging datasets. In addition, we also provide detailed proofs for the proposed theoretical analysis framework. The source code can be found in https://github.com/dtuzi123/Expansion-Graph-Model.