Recently, images are considered samples from a high-dimensional distribution, and deep learning has become almost synonymous with image generation. However, is a deep learning network truly necessary for image generation? In this paper, we investigate the possibility of image generation without using a deep learning network, motivated by validating the assumption that images follow a high-dimensional distribution. Since images are assumed to be samples from such a distribution, we utilize the Gaussian Mixture Model (GMM) to describe it. In particular, we employ a recent distribution learning technique named as Monte-Carlo Marginalization to capture the parameters of the GMM based on image samples. Moreover, we also use the Singular Value Decomposition (SVD) for dimensionality reduction to decrease computational complexity. During our evaluation experiment, we first attempt to model the distribution of image samples directly to verify the assumption that images truly follow a distribution. We then use the SVD for dimensionality reduction. The principal components, rather than raw image data, are used for distribution learning. Compared to methods relying on deep learning networks, our approach is more explainable, and its performance is promising. Experiments show that our images have a lower FID value compared to those generated by variational auto-encoders, demonstrating the feasibility of image generation without deep learning networks.

翻译：最近，图像被视为从高维分布中采样的样本，深度学习几乎已成为图像生成的代名词。然而，图像生成是否真的需要深度学习网络？本文旨在验证图像服从高维分布这一假设，探讨在不使用深度学习网络的情况下实现图像生成的可行性。由于假设图像是来自高维分布的样本，我们采用高斯混合模型（GMM）来描述该分布。具体而言，我们引入一种名为蒙特卡洛边缘化的最新分布学习方法，基于图像样本捕获GMM的参数。此外，我们还利用奇异值分解（SVD）进行降维，以降低计算复杂度。在评估实验中，我们首先尝试直接对图像样本的分布进行建模，以验证图像确实服从某一分布的假设；随后使用SVD进行降维，并利用主成分（而非原始图像数据）进行分布学习。与依赖深度学习网络的方法相比，我们的方法更具可解释性，且性能表现良好。实验表明，相比变分自编码器生成的图像，我们的方法生成的图像具有更低的FID值，从而证明了无需深度学习网络实现图像生成的可行性。