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..
Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning
Authors: Xiyu Wang, Baijiong Lin, Daochang Liu, Ying-Cong Chen, Chang Xu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments in the context of few-shot image generation tasks demonstrate that our method is efficient and excels in terms of image quality and diversity compared to existing GAN-based and DPM-based methods. |
| Researcher Affiliation | Academia | 1School of Computer Science, Faculty of Engineering, The University of Sydney, Australia 2The Hong Kong University of Science and Technology (Guangzhou), China. |
| Pseudocode | Yes | Algorithm 1 Training DPMs with ANT |
| Open Source Code | Yes | The code is available at https://github.com/ShinyGua/DPMs-ANT. |
| Open Datasets | Yes | Following (Ojha et al., 2021), we use FFHQ (Karras et al., 2020b) and LSUN Church (Yu et al., 2015) as source datasets. |
| Dataset Splits | No | The paper mentions using a "limited set of just 10 training images" for few-shot tasks, but it does not explicitly provide information on specific training, validation, or test dataset splits (e.g., percentages or counts for each split). |
| Hardware Specification | No | The acknowledgements mention the use of "National Computational Infrastructure (NCI)" and "Sydney Informatics Hub HPC Allocation Scheme," indicating high-performance computing resources were used, but specific hardware details such as GPU/CPU models or memory amounts are not provided. |
| Software Dependencies | No | The paper mentions frameworks like DDPM, LDM, and the Style GAN2 codebase but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set c = 4 and d = 8 for DDPMs, while c = 2 and d = 8 for LDMs. ... For similarity-guided training, we set γ = 5. ... For adversarial noise selection, we set J = 10 and ω = 0.02. We employ a learning rate of 5e-5 for DDPMs and 1e-5 for LDMs to train with approximately 300 iterations and a batch size of 40. |