Compositional Zero-Shot Artistic Font Synthesis
Authors: Xiang Li, Lei Wu, Changshuo Wang, Lei Meng, Xiangxu Meng
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 202035260@mail.sdu.edu.cn, i lily@sdu.edu.cn, 202115242@mail.sdu.edu.cn, lmeng@sdu.edu.cn, mxx@sdu.edu.cn |
| 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. |