ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for
  Ensemble Learning
Shi Zhenning Dong Changsheng Xie Xueshuo Pan Bin He Along Li Tao
Nowadays, denoising diffusion probabilistic models have been adapted for many
image segmentation tasks. However, existing end-to-end models have already
demonstrated remarkable capabilities. Rather than using denoising diffusion
probabilistic models alone, integrating the abilities of both denoising
diffusion probabilistic models and existing end-to-end models can better
improve the performance of image segmentation. Based on this, we implicitly
introduce residual term into the diffusion process and propose
ResEnsemble-DDPM, which seamlessly integrates the diffusion model and the
end-to-end model through ensemble learning. The output distributions of these
two models are strictly symmetric with respect to the ground truth
distribution, allowing us to integrate the two models by reducing the residual
term. Experimental results demonstrate that our ResEnsemble-DDPM can further
improve the capabilities of existing models. Furthermore, its ensemble learning
strategy can be generalized to other downstream tasks in image generation and
get strong competitiveness.