Minimizing Trajectory Curvature of ODE-based Generative Models
Authors: Sangyun Lee, Beomsu Kim, Jong Chul Ye
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 <jong.ye@kaist.ac.kr>. |
| 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). |