Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..

Poisson Flow Generative Models

Authors: Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola

NeurIPS 2022 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experimentally, PFGM achieves current state-of-the-art performance among the normalizing flow models on CIFAR-10, with an Inception score of 9.68 and a FID score of 2.35.
Researcher Affiliation Academia Massachusetts Institute of Technology EMAIL; EMAIL
Pseudocode Yes Algorithm 1: Learning the normalized Poisson Field
Open Source Code Yes The code is available at https: //github.com/Newbeeer/poisson_flow.
Open Datasets Yes For image generation tasks, we consider the CIFAR-10 [22], Celeb A 64 64 [38] and LSUN bedroom 256 256 [39].
Dataset Splits Yes We follow the training procedure in [33] and split the training data into 99% training and 1% validation sets for model selection.
Hardware Specification Yes All the experiments are run on a single NVIDIA A100 GPU.
Software Dependencies No The paper mentions 'Scipy library [37] with the RK45 [7] method' but does not provide specific version numbers for software dependencies.
Experiment Setup Yes We choose M = 291 (CIFAR-10 and Celeb A) 356 (LSUN bedroom), σ = 0.01 and = 0.03 for the perturbation Algorithm 2, and zmin = 1e 3, zmax = 40 (CIFAR-10) 60 (Celeb A 642) 100 (LSUN bedroom) for the backward ODE. We further clip the norms of initial samples into (0,3000) for CIFAR-10, (0,6000) for Celeb A 642 and (0,30000) for LSUN bedroom.