Domain Expansion of Image Generators

Abstract

Can one inject new concepts into an already trained generative model, while respecting its existing structure and knowledge? We propose a new task -- domain expansion -- to address this. Given a pretrained generator and novel (but related) domains, we expand the generator to jointly model all domains, old and new, harmoniously. First, we note the generator contains a meaningful, pretrained latent space. Is it possible to minimally perturb this hard-earned representation, while maximally representing the new domains? Interestingly, we find that the latent space offers unused, "dormant" axes, which do not affect the output. This provides an opportunity -- by "repurposing" these axes, we are able to represent new domains, without perturbing the original representation. In fact, we find that pretrained generators have the capacity to add several -- even hundreds -- of new domains! Using our expansion technique, one "expanded" model can supersede numerous domain-specific models, without expanding model size. Additionally, using a single, expanded generator natively supports smooth transitions between and composition of domains.