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 [1].
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models
Authors: Yangming Li, Boris van Breugel, Mihaela van der Schaar
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on multiple image datasets show that SMD significantly improves different types of diffusion models (e.g., DDPM), espeically in the situation of few backward iterations. and 5 EXPERIMENTS Let us verify how SMD improves the quality and speed of existing diffusion models. First, we use a toy example to visualise that existing diffusion models struggle to learn multivariate Gaussians, whereas SMD does not. Subsequently, we show how SMD significantly improves the FID score across different types of diffusion models (e.g., DDPM, ADM (Dhariwal & Nichol, 2021), and LDM) and datasets. |
| Researcher Affiliation | Academia | Yangming Li, Boris van Breugel, Mihaela van der Schaar Department of Applied Mathematics and Theoretical Physics University of Cambridge EMAIL |
| Pseudocode | Yes | Algorithm 1 Training and Algorithm 2 Sampling |
| Open Source Code | Yes | The source code of this work is publicly available at a personal repository: https://github.com/louisli321/smd, and our lab repository: https://github.com/vanderschaarlab/smd. |
| Open Datasets | Yes | Datasets include CIFAR-10 (Krizhevsky et al., 2009), LSUN-Conference, LSUN-Church (Yu et al., 2015), and Celeb A-HQ (Liu et al., 2015). |
| Dataset Splits | No | The paper mentions using datasets for evaluation but does not provide specific details on training, validation, or test splits (e.g., percentages or counts for each split). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions various models and networks (e.g., U-Net) and cites frameworks, but does not list specific software dependencies with version numbers required for reproduction. |
| Experiment Setup | No | The paper mentions some parameters like 'T = 1000' and '100 backward iterations', but it lacks a comprehensive description of the experimental setup, including specific hyperparameters like learning rate, batch size, or optimizer settings. |