Cloud Model Characteristic Function Auto-Encoder: Integrating Cloud Model Theory with MMD Regularization for Enhanced Generative Modeling

TL;DR

This paper introduces a new generative auto-encoder model that uses cloud model theory and characteristic functions to better capture complex data distributions, improving sample quality and diversity.

Contribution

It integrates cloud model theory into the WAE framework using a novel characteristic function regularizer, providing a flexible prior that enhances generative performance.

Findings

Outperforms existing models on multiple datasets in quality and diversity.

Provides a new cloud model prior for latent space regularization.

Demonstrates improved reconstruction and latent space structure.

Abstract

We introduce Cloud Model Characteristic Function Auto-Encoder (CMCFAE), a novel generative model that integrates the cloud model into the Wasserstein Auto-Encoder (WAE) framework. By leveraging the characteristic functions of the cloud model to regularize the latent space, our approach enables more accurate modeling of complex data distributions. Unlike conventional methods that rely on a standard Gaussian prior and traditional divergence measures, our method employs a cloud model prior, providing a more flexible and realistic representation of the latent space, thus mitigating the homogenization observed in reconstructed samples. We derive the characteristic function of the cloud model and propose a corresponding regularizer within the WAE framework. Extensive quantitative and qualitative evaluations on MNIST, FashionMNIST, CIFAR-10, and CelebA demonstrate that CMCFAE outperforms existing models in terms of reconstruction quality, latent space structuring, and sample diversity. This work not only establishes a novel integration of cloud model theory with MMD-based regularization but also offers a promising new perspective for enhancing autoencoder-based generative models.