Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors
Authors: Yazid Janati, Badr MOUFAD, Alain Durmus, Eric Moulines, Jimmy Olsson
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the versatility and effectiveness of our approach for a wide range of Bayesian inverse problems. The code is available at https://github.com/Badr-MOUFAD/dcps |
| Researcher Affiliation | Collaboration | Yazid Janati ,1 Badr Moufad ,1 Alain Durmus1 Eric Moulines1,3 Jimmy Olsson2 1 CMAP, Ecole polytechnique 2 KTH Royal Institute of Technology 3 MBZUAI |
| Pseudocode | Yes | The pseudo-code of the DCPS algorithm is in Algorithm 1. |
| Open Source Code | Yes | The code is available at https://github.com/Badr-MOUFAD/dcps |
| Open Datasets | Yes | For these imaging experiments, we use the FFHQ256 [23] and Image Net256 [14] datasets and the publicly available pre-trained models of [8] and [15]. |
| Dataset Splits | Yes | The UCY-student dataset was split int a train and a validation sets with 1450 and 140 trajectories respectively. |
| Hardware Specification | Yes | The memory consumption is measured by how many samples each algorithm can generate in parallel on a single 48GB L40S NVIDIA GPU for the Diffusion model trained on FFHQ [15]. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | For all the experiments we implement Algorithm 1. We use the same parameters K = 2, L = 3 and ΞΆ = 1 for all the experiments. For the number of Langevin steps, we set it to M = 50 and M = 500 (respectively) for the Gaussian mixture experiment and M = 5 for the imaging and trajectory inpainting experiments. |