  This paper presents LatentPatch, a new method for generating realistic images
from a small dataset of only a few images. We use a lightweight model with only
a few thousand parameters. Unlike traditional few-shot generation methods that
finetune pre-trained large-scale generative models, our approach is computed
directly on the latent distribution by sequential feature matching, and is
explainable by design. Avoiding large models based on transformers, recursive
networks, or self-attention, which are not suitable for small datasets, our
method is inspired by non-parametric texture synthesis and style transfer
models, and ensures that generated image features are sampled from the source
distribution. We extend previous single-image models to work with a few images
and demonstrate that our method can generate realistic images, as well as
enable conditional sampling and image editing. We conduct experiments on face
datasets and show that our simplistic model is effective and versatile.
