FontRL: Chinese Font Synthesis via Deep Reinforcement Learning
Authors: Yitian Liu, Zhouhui Lian2198-2206
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both quantitative and qualitative experimental results demonstrate the superiority of the proposed Font RL compared to the state of the art. |
| Researcher Affiliation | Academia | Wangxuan Institute of Computer Technology, Peking University, Beijing, P.R. China {lsflyt, lianzhouhui}@pku.edu.cn |
| Pseudocode | No | The paper describes its methodology in text and through architectural diagrams (Figure 1 and Figure 2) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/lsflytpku/Font RL. |
| Open Datasets | Yes | In our experiments, we directly use the dataset introduced in (Jiang et al. 2019), which consists of glyph images of all 6763 Chinese characters and their manually-specified stroke skeletons in 5 different font styles as target and a mean font style as reference. |
| Dataset Splits | No | The paper mentions using 'an input character set proposed in (Lian, Zhao, and Xiao 2016) for training' consisting of '775 Chinese characters' but does not specify the train/validation/test splits or their proportions for the dataset. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions learning rates and hyperparameters but does not provide specific software dependencies with version numbers, such as programming languages or deep learning framework versions. |
| Experiment Setup | Yes | The learning rate of MPNet is initialized as 0.0003, and decayed to 0.0001 after 6000 iterations; the learning rate of BBox Net is initialized as 0.001, decayed to 0.0005 after 40 epochs, and then decayed to 0.0001 after 100 epochs; the hyper-parameters of Style Net are set to the default values. |