  Being different from deep-learning-based (DL-based) image generation methods,
a new image generative model built upon successive subspace learning principle
is proposed and named GenHop (an acronym of Generative PixelHop) in this work.
GenHop consists of three modules: 1) high-to-low dimension reduction, 2) seed
image generation, and 3) low-to-high dimension expansion. In the first module,
it builds a sequence of high-to-low dimensional subspaces through a sequence of
whitening processes, each of which contains samples of joint-spatial-spectral
representation. In the second module, it generates samples in the lowest
dimensional subspace. In the third module, it finds a proper high-dimensional
sample for a seed image by adding details back via locally linear embedding
(LLE) and a sequence of coloring processes. Experiments show that GenHop can
generate visually pleasant images whose FID scores are comparable or even
better than those of DL-based generative models for MNIST, Fashion-MNIST and
CelebA datasets.
