用云模型提升生成模型，让样本更真实多样。
Cloud Model Characteristic Function Auto-Encoder: Integrating Cloud Model Theory with MMD Regularization for Enhanced Generative Modeling
- 用云模型替代传统高斯先验，优化潜在空间结构
- 在多个数据集上重建质量与样本多样性均超越现有方法
- 适合关注生成模型潜空间建模的科研人员
我们提出云模型特征函数自编码器（CMCFAE），将云模型引入Wasserstein自编码器（WAE）框架。通过利用云模型的特征函数对潜在空间进行正则化，该方法能更准确地建模复杂数据分布。与依赖标准高斯先验和传统散度度量的常规方法不同，本方法采用云模型先验，提供更灵活、更真实的潜在空间表示，从而缓解重构样本同质化问题。我们推导了云模型的特征函数，并在WAE框架中提出相应的正则项。在MNIST、FashionMNIST、CIFAR-10和CelebA上的大量定量与定性评估表明，CMCFAE在重建质量、潜在空间结构及样本多样性方面均优于现有模型。本工作不仅建立了云模型理论与基于MMD正则化的新型融合方式，也为增强自编码器生成模型提供了新思路。
原文摘要 · Abstract (English)
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.
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