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..
SyFormer: Structure-Guided Synergism Transformer for Large-Portion Image Inpainting
Authors: Jie Wu, Yuchao Feng, Honghui Xu, Chuanmeng Zhu, Jianwei Zheng
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on two publicly available datasets, i.e., Celeb A-HQ and Places2, to qualitatively and quantitatively demonstrate the superiority of our model over state-of-the-arts. |
| Researcher Affiliation | Academia | 1Zhejiang University of Technology 2Zhejiang University |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | Two well-known datasets, i.e., Celeb A-HQ (Karras et al. 2018) and Place2 (Zhou et al. 2017), are used for the performance investigation. |
| Dataset Splits | Yes | The Celeb A-HQ data is split into training, validation, and test sets in a ratio of 24:1:5. We keep 220,000 and 5000 images from the original places2 sets for training and testing, respectively. |
| Hardware Specification | Yes | All experiments are conducted on two GPUs of RTX 3090 with a single 12G of video memory. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | All experiments are conducted using Py Torch with a batch size of 8. Our model is optimized by Adam with a learning rate of 2 10 4. The hyper-parameters in Eq. (15) are set as λadv = 0.1, λrec = 40, λsty = 120, λper = 0.05 to generate the sensuously optimal results. |