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 Task Difficulty-based Curriculum for Training Diffusion Models
Authors: Jin-Young Kim, Hyojun Go, Soonwoo Kwon, Hyun-Gyoon Kim
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate these advantages through comprehensive experiments in image generation tasks, including unconditional, class-conditional, and text-to-image generation. |
| Researcher Affiliation | Academia | Jin-Young Kim Hyojun Go Soonwoo Kwon Hyun-Gyoon kim1 Ajou University1 EMAIL, EMAIL |
| Pseudocode | Yes | The detailed process and overall curriculum learning procedure are outlined in Algorithm ?? and ?? in Appendix D, respectively. |
| Open Source Code | No | The paper does not provide a direct link to a source-code repository, an explicit statement about code release, or mention code in supplementary materials. |
| Open Datasets | Yes | By integrating our curriculum learning strategy into architectures Di T (Peebles & Xie, 2022), EDM (Karras et al., 2022), and Si T (Ma et al., 2024) we demonstrate the efficacy of our approach in enhancing performance, accelerating convergence speed, and maintaining compatibility with existing techniques. . . . These include unconditional generation, classconditional generation, and text-to-image generation, utilizing datasets such as FFHQ (Karras et al., 2019), Image Net (Deng et al., 2009), and MS-COCO (Lin et al., 2014). |
| Dataset Splits | Yes | utilizing datasets such as FFHQ (Karras et al., 2019), Image Net (Deng et al., 2009), and MS-COCO (Lin et al., 2014). By integrating our curriculum learning strategy into architectures Di T (Peebles & Xie, 2022), EDM (Karras et al., 2022), and Si T (Ma et al., 2024) we demonstrate the efficacy of our approach in enhancing performance, accelerating convergence speed, and maintaining compatibility with existing techniques. . . . For our comprehensive evaluation of various methods, we employed three distinct image-generation tasks: 1) Unconditional generation with the FFHQ dataset (Karras et al., 2019), 2) Class-conditional generation with CIFAR-10 (Krizhevsky et al., 2009) and Image Net (Deng et al., 2009) datasets, and 3) Text-to-Image generation with MS-COCO dataset (Lin et al., 2014). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We examined the robustness of the proposed curriculum training with respect to hyper-parameters: the number of clusters N and the maximum patience τ. As shown in Fig. 4, our method consistently outperforms the vanilla model, and the best result is observed at N = 20, τ = 200. . . . We trained Di T-L/2 with 2M iterations and reported the results in Table 2. |