  Few-shot image generation aims to train generative models using a small
number of training images. When there are few images available for training
(e.g. 10 images), Learning From Scratch (LFS) methods often generate images
that closely resemble the training data while Transfer Learning (TL) methods
try to improve performance by leveraging prior knowledge from GANs pre-trained
on large-scale datasets. However, current TL methods may not allow for
sufficient control over the degree of knowledge preservation from the source
model, making them unsuitable for setups where the source and target domains
are not closely related. To address this, we propose a novel pipeline called
Peer is your Pillar (PIP), which combines a target few-shot dataset with a peer
dataset to create a data-unbalanced conditional generation. Our approach
includes a class embedding method that separates the class space from the
latent space, and we use a direction loss based on pre-trained CLIP to improve
image diversity. Experiments on various few-shot datasets demonstrate the
advancement of the proposed PIP, especially reduces the training requirements
of few-shot image generation.
