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..
Minimizing Trajectory Curvature of ODE-based Generative Models
Authors: Sangyun Lee, Beomsu Kim, Jong Chul Ye
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method achieves a lower curvature than previous models and, therefore, decreased sampling costs while maintaining competitive performance. |
| Researcher Affiliation | Academia | 1Soongsil University 2KAIST. Correspondence to: Jong Chul Ye <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/sangyun884/fast-ode. |
| Open Datasets | Yes | We further conduct an experiment on the image dataset... MNIST, CIFAR-10, and Celeb AHQ (64 64) datasets... FFHQ 64 64, AFHQ 64 64, and Celeb AHQ 256 256 datasets. |
| Dataset Splits | No | The paper uses standard datasets like CIFAR-10, MNIST, FFHQ, AFHQ, and Celeb AHQ, which have predefined splits. However, the paper does not explicitly specify the training/validation/test dataset splits (e.g., percentages, sample counts, or explicit citation for splits) used to reproduce the experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using DDPM++ architecture, Adam optimizer, and Scipy's RK45 method, but it does not specify concrete version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Table 5 shows the training and architecture configuration we use in our experiments. (This table includes Iterations, Batch size, Learning rate, LR warm-up steps, EMA decay rate, EMA start steps, Dropout probability, Channel multiplier, Channels per resolution, # of Res Blocks, t range). |