  This work presents a generative modeling approach based on successive
subspace learning (SSL). Unlike most generative models in the literature, our
method does not utilize neural networks to analyze the underlying source
distribution and synthesize images. The resulting method, called the
progressive attribute-guided extendable robust image generative (PAGER) model,
has advantages in mathematical transparency, progressive content generation,
lower training time, robust performance with fewer training samples, and
extendibility to conditional image generation. PAGER consists of three modules:
core generator, resolution enhancer, and quality booster. The core generator
learns the distribution of low-resolution images and performs unconditional
image generation. The resolution enhancer increases image resolution via
conditional generation. Finally, the quality booster adds finer details to
generated images. Extensive experiments on MNIST, Fashion-MNIST, and CelebA
datasets are conducted to demonstrate generative performance of PAGER.
