Pose-preserving Cross Spectral Face Hallucination
Authors: Junchi Yu, Jie Cao, Yi Li, Xiaofei Jia, Ran He
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three heterogeneous face datasets demonstrate that our approach not only outperforms current state-of-the-art HFR methods but also produce visually appealing results at a high resolution (256 256). |
| Researcher Affiliation | Collaboration | 1NLPR&CRIPAC Institute of Automation, Chinese Academy of Sciences, China 2University of Chinese Academy of Sciences, China 3Center for Excellence in Brain Science and Intelligence Technology, CAS, China 4Central Media Technology Institute, Huawei Technology Co., Ltd., China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | The CASIA NIR-VIS 2.0 face database [Li et al., 2013] contains 725 subjects... The Oulu-CASIA NIR-VIS database [Chen et al., 2009] contains 80 subjects... The BUAA-VISNIR face database [Huang et al., 2012] consists of 150 subjects... |
| Dataset Splits | No | The paper mentions training and testing sets but does not explicitly provide details for a validation split (percentages, counts, or specific predefined validation sets). |
| Hardware Specification | Yes | Our end-to-end network is trained on the CASIA NIRVIS 2.0 face database on an NVIDIA Titan XP GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., libraries, frameworks, or programming languages). |
| Experiment Setup | Yes | The hyperparameters from λ1 to λ5 are assigned as 2.5 10 5, 0.002, 0.1, 2.5 10 5, 0.1 respectively. |