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..
Compositional Zero-Shot Artistic Font Synthesis
Authors: Xiang Li, Lei Wu, Changshuo Wang, Lei Meng, Xiangxu Meng
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of our model in generating high-quality artistic font images with unseen style compositions against other state-of-theart methods. |
| Researcher Affiliation | Academia | Xiang Li1, Lei Wu1 , Changshuo Wang1, Lei Meng1,2 , Xiangxu Meng1 1School of Software, Shandong University 2Shandong Research Institute of Industrial Technology EMAIL, i EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methods but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The source code and data is available at moonlight03.github.io/CAFS-GAN/. |
| Open Datasets | Yes | SSAF Dataset. SSAF [Li et al., 2022b] contains a large number of high-quality Chinese and English artistic images, with annotations for their glyphs, effects, and content. Fonts Dataset. Fonts [Ge et al., 2021] is a computer generated RGB font image dataset. |
| Dataset Splits | No | The paper specifies 775 Chinese characters and 22 uppercase English letters for training, and 197 Chinese characters and 4 uppercase English letters for testing, but does not explicitly mention a validation split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | The hyperparameters are set as: λadv = 1.0 and λsty = 0.1. In training, we set the batch size as 8 and train 105 iterations for Chinese artistic font generation and 2 104 iterations for English. The learning rate is set to 0.0001, using Adam optimizer. |