  How can we apply human feedback into generative model? As answer of this
question, in this paper, we show the method applied on denoising problem and
domain adaptation using human feedback. Deep generative models have
demonstrated impressive results in image denoising. However, current image
denoising models often produce inappropriate results when applied to domains
different from the ones they were trained on. If there are `Good' and `Bad'
result for unseen data, how to raise up quality of `Bad' result. Most methods
use an approach based on generalization of model. However, these methods
require target image for training or adapting unseen domain. In this paper, to
adapting domain, we deal with non-target image for unseen domain, and improve
specific failed image. To address this, we propose a method for fine-tuning
inappropriate results generated in a different domain by utilizing human
feedback. First, we train a generator to denoise images using only the noisy
MNIST digit '0' images. The denoising generator trained on the source domain
leads to unintended results when applied to target domain images. To achieve
domain adaptation, we construct a noise-image denoising generated image data
set and train a reward model predict human feedback. Finally, we fine-tune the
generator on the different domain using the reward model with auxiliary loss
function, aiming to transfer denoising capabilities to target domain. Our
approach demonstrates the potential to efficiently fine-tune a generator
trained on one domain using human feedback from another domain, thereby
enhancing denoising abilities in different domains.
