Implicit Denoising

Implicit denoising focuses on leveraging the inherent noise-reduction capabilities of machine learning models, particularly deep learning architectures, without explicit denoising training. Current research explores this phenomenon in various domains, including text-to-image and text-to-3D generation, image and depth map processing, and graph neural networks, often employing diffusion models, implicit neural representations, and novel network architectures to enhance denoising performance. This research is significant because it improves the robustness and accuracy of models across diverse applications by implicitly handling noisy data, reducing the need for extensive, often costly, explicit denoising strategies. The resulting improvements in model efficiency and performance have broad implications for various fields, from computer vision and 3D modeling to natural language processing.