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..
Constant Acceleration Flow
Authors: Dogyun Park, Sojin Lee, Sihyeon Kim, Taehoon Lee, Youngjoon Hong, Hyunwoo J. Kim
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive studies on toy datasets, CIFAR-10, and Image Net 64 64 demonstrate that CAF outperforms state-of-the-art baselines for one-step generation. We also show that CAF dramatically improves few-step coupling preservation and inversion over Rectified flow. |
| Researcher Affiliation | Academia | Dogyun Park Korea University EMAIL Sojin Lee Korea University EMAIL Sihyeon Kim Korea University EMAIL Taehoon Lee Korea University EMAIL Youngjoon Hong KAIST EMAIL Hyunwoo J. Kim Korea University EMAIL |
| Pseudocode | Yes | Algorithm 1 Training process of Constant Acceleration Flow |
| Open Source Code | Yes | Code is available at https://github.com/mlvlab/CAF. |
| Open Datasets | Yes | Our comprehensive studies on toy datasets, CIFAR-10, and Image Net 64 64 demonstrate that CAF outperforms state-of-the-art baselines for one-step generation. |
| Dataset Splits | No | To further validate the effectiveness of our approach, we train CAF on real-world image datasets, specifically CIFAR-10 at 32 32 resolution and Image Net at 64 64 resolution. |
| Hardware Specification | Yes | The total training takes about 21 days with 8 NVIDIA A100 GPUs for Image Net, and takes 10 days 8 NVIDIA RTX3090 GPUs for CIFAR-10. |
| Software Dependencies | No | For all experiments, we use Adam W [53] optimizer with a learning rate of 0.0001 and apply an Exponential Moving Average (EMA) with a 0.999 decay rate. |
| Experiment Setup | Yes | For all experiments, we use Adam W [53] optimizer with a learning rate of 0.0001 and apply an Exponential Moving Average (EMA) with a 0.999 decay rate. For adversarial training, we employ adversarial loss Lgan using real data x1,real from [24]: ... We set h = 1.5 and d as LPIPS-Huber loss [43] for all real-data experiments. |