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..
Denoising Diffusion Step-aware Models
Authors: Shuai Yang, Yukang Chen, Luozhou Wang, Shu Liu, Ying-Cong Chen
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations demonstrate that DDSM achieves computational savings of 49% for CIFAR-10, 61% for Celeb A-HQ, 59% for LSUN-bedroom, 71% for AFHQ, and 76% for Image Net, all without compromising the generation quality. |
| Researcher Affiliation | Collaboration | Shuai Yang 1,3 Yukang Chen 2 Luozhou Wang 1,3 Shu Liu 4 Yingcong Chen 1,5 1HKUST(GZ) 2CUHK 3HKUST(GZ) Smart More Joint Lab 4Smart More 5HKUST |
| Pseudocode | Yes | Algorithm 1 DDSM Training; Algorithm 2 DDSM Evolutionary Searching |
| Open Source Code | Yes | Our code and models are available at https://github.com/EnVision-Research/DDSM. |
| Open Datasets | Yes | We conduct experiments on five image datasets on different domains, ranging from small scale to large scale. They are CIFAR10 (Krizhevsky et al., 2009), Celeb A-HQ (64x64, 128x128) (Liu et al., 2015), LSUN-bedroom (Yu et al., 2016), AFHQ (Choi et al., 2020), and Image Net (Deng et al., 2009). |
| Dataset Splits | No | No explicit statement providing percentages or sample counts for training, validation, and test splits was found, although training data is mentioned. For the CIFAR-10 dataset, we utilized the standard split of 50,000 images designated for training. For the Celeb A-HQ-64, AFHQ, and LSUN-bedroom datasets, we adhered to the typical training data splits. |
| Hardware Specification | Yes | The GPU latency is the time cost of generating one image with a single NVIDIA RTX 3090. |
| Software Dependencies | No | No specific version numbers for software dependencies (e.g., pymoo, pytorch) are provided, only the names of the tools and their reference papers. |
| Experiment Setup | Yes | Regarding the sizes of the sub-networks, we offer seven different options, corresponding to 2/8 of the original ADM’s width... For the search parameters, the process encompasses a total of 10 iterations, with each iteration involving a population of 50, and maintaining a mutation rate of 0.001... For CIFAR-10, we set the weight parameter to 0.1 to favor higher image quality, while for Celeb A, the FLOPs weight parameter is adjusted to 0.25. |