mlsanityData to 7 Oct 2026

ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning

arXiv 2312.01682 ↗First posted 4 Dec 2023cs.CV cs.AIcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 39 of its 45 references that are in the map. First posted on arXiv 4 Dec 2023.

Builds on

39 of 45 references.

Closest ancestors · 6

Same direction, newest first

  1. 2022Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning8 Aug
  2. 2022Classifier-Free Diffusion Guidance26 Jul
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  4. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  5. 2020Denoising Diffusion Probabilistic Models19 Jun
  6. 2020Improved Techniques for Training Score-Based Generative Models16 Jun

Other references · 33

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21 more
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