Mean-field Chaos Diffusion Models
Authors: Sungwoo Park, Dongjun Kim, Ahmed Alaa
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Empirical Study This section provides a numerical validation of the efficacy of integrating MFT into the SGM framework, particularly in extreme scenarios of large cardinality, where previous works struggle to achieve robust performance. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Sciences, UC Berkeley 2Department of Computer Science, Stanford 3UCSF. |
| Pseudocode | Yes | A.9.1. TRAINING MEAN-FIELD CHAOTIC DIFFUSION MODELS This section aims to present the algorithmic implementation of mean-field score matching and training procedure with objective (P3). A.9.2. SAMPLING SCHEME FOR MEAN-FIELD CHAOS DIFFUSION MODELS To sample the denoising dynamics, this work proposes a modified Euler scheme, adapted for mean-field interacting particle systems (Bossy & Talay, 1997; dos Reis et al., 2022), and approximate the stochastic differential equations in the mean-field limit. The proposed scheme involves a four-step sampling procedure. |
| Open Source Code | No | No explicit statement or link regarding the release of the source code for the methodology described in this paper was found. |
| Open Datasets | Yes | Datasets. This paper utilizes Shape Net, a widely recognized dataset comprising a vast collection of 3D object models across multiple categories, and Med Shape Net, a curated collection of medical shape data designed for advanced imaging analysis. 1. Shape Net. (Chang et al., 2015) ... 2. Med Shape Net. (Li et al., 2023) |
| Dataset Splits | No | No explicit train/test/validation dataset splits with specific percentages or counts were found. |
| Hardware Specification | Yes | All experiments were conducted using a setup of 4 NVIDIA A100 GPUs. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8) were explicitly mentioned in the paper. |
| Experiment Setup | Yes | Table 4. Hyperparameters according to cardinality in data instances. Learning Rate 1.0e 3 1.0e 4 (VP SDE) σ2 t = βt, βt = βmin + t(βmax βmin), βmax = 20.0, βmin = 0.1 (Diffusion Steps) K {1, , 300}, |K| = 300 (Branching Ratio) b 2 (Branching Steps) K {100, 200} {50, 100, 150, 200} {50, 100, 150, 200, 250} (Initial Cardinality) {N0} 250 625 1250 3125 (Interaction Degree) k 10 3 3 3 |