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..
EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching
Authors: Xinwang Chen, Ning Liu, Yichen Zhu, Feifei Feng, Jian Tang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate the efficacy of EDT. |
| Researcher Affiliation | Collaboration | Xinwang Chen 1, Ning Liu 1, Yichen Zhu1, Feifei Feng1, Jian Tang 2 1 Midea Group, 2 Beijing Innovation Center of Humanoid Robotics EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at here. |
| Open Datasets | Yes | The training dataset is Image Net [32] with 256 256 and 512 512 resolution. |
| Dataset Splits | No | The paper mentions 'The training dataset is Image Net [32] with 256 256 and 512 512 resolution' but does not explicitly provide training/validation/test dataset splits needed to reproduce the experiment. |
| Hardware Specification | Yes | Training is conducted on eight L40 48GB GPUs, while the speed test for inference is performed on a single L40 48GB GPU. |
| Software Dependencies | No | The paper mentions 'Tensor Flow evaluation suite from ADM [4]' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | EDT uses the Adan [34] optimizer with a global batch size of 256 and without weight decay. The learning rate linearly decreases from 1e-3 to 5e-5 over 400k iterations. Masking training strategy: We set the mask ratio 0.4-0.5 in the first down-sampling module, and 0.1-0.2 in the second. |