Markov Random Neural Fields for Face Sketch Synthesis
Authors: Mingjin Zhang, Nannan Wang, Xinbo Gao, Yunsong Li
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the Chinese University of Hong Kong face sketch database illustrate that our proposed framework can preserve the common structure and capture the characteristic features. Compared with the state-of-the-art methods, our method achieves better results in terms of both quantitative and qualitative experimental evaluations. and 4 Experimental Results and Analysis |
| Researcher Affiliation | Academia | Mingjin Zhang , Nannan Wang , Xinbo Gao , Yunsong Li State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi an 710071, China State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian University, Xi an 710071, China {mjinzhang, nnwang}@xidian.edu.cn, {xbgao, ysli}@mail.xidian.edu.cn |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any concrete access information (link, explicit statement of release) to open-source code for the described methodology. |
| Open Datasets | Yes | We conduct the experiments on the Chinese University of Hong Kong (CUHK) face sketch database (CUFS) [Wang and Tang, 2009]. |
| Dataset Splits | Yes | In the CUHK student database, 88 pairs are randomly selected to form the training set. And the remaining 100 pairs are used for model test. In the AR database, we choose 100 pairs for model training and the remaining 23 pairs for model test. In the XM2VTS database, we select 100 pairs for training, and the remaining 195 pairs as the test data. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running the experiments were found. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the model formulation and optimization methods but does not provide specific hyperparameter values or detailed training configurations (e.g., learning rates, batch sizes, epochs) for the experimental setup. |