  Large, pre-trained generative models have been increasingly popular and
useful to both the research and wider communities. Specifically, BigGANs a
class-conditional Generative Adversarial Networks trained on
ImageNet---achieved excellent, state-of-the-art capability in generating
realistic photos. However, fine-tuning or training BigGANs from scratch is
practically impossible for most researchers and engineers because (1) GAN
training is often unstable and suffering from mode-collapse; and (2) the
training requires a significant amount of computation, 256 Google TPUs for 2
days or 8xV100 GPUs for 15 days. Importantly, many pre-trained generative
models both in NLP and image domains were found to contain biases that are
harmful to society. Thus, we need computationally-feasible methods for
modifying and re-purposing these huge, pre-trained models for downstream tasks.
In this paper, we propose a cost-effective optimization method for improving
and re-purposing BigGANs by fine-tuning only the class-embedding layer. We show
the effectiveness of our model-editing approach in three tasks: (1)
significantly improving the realism and diversity of samples of complete
mode-collapse classes; (2) re-purposing ImageNet BigGANs for generating images
for Places365; and (3) de-biasing or improving the sample diversity for
selected ImageNet classes.
