Domain Adaptation Based on Human Feedback for Enhancing Image Denoising in Generative Models

How can human feedback be effectively integrated into generative models? This study addresses this question by proposing a method to enhance image denoising and achieve domain adaptation using human feedback. Deep generative models, while achieving remarkable performance in image denoising within training domains, often fail to generalize to unseen domains. To overcome this limitation, we introduce a novel approach that fine-tunes a denoising model using human feedback without requiring labeled target data. Our experiments demonstrate a significant improvement in denoising performance. For example, on the Fashion-MNIST test set, the peak signal-to-noise ratio (PSNR) increased by 94%, with an average improvement of 1.61 ± 2.78 dB and a maximum increase of 18.21 dB. Additionally, the proposed method effectively prevents catastrophic forgetting, as evidenced by the consistent performance on the original MNIST domain. By leveraging a reward model trained on human preferences, we show that the quality of denoised images can be significantly improved, even when applied to unseen target data. This work highlights the potential of human feedback for efficient domain adaptation in generative models, presenting a scalable and data-efficient solution for enhancing performance in diverse domains.