Translate the Facial Regions You Like Using Self-Adaptive Region Translation
Authors: Wenshuang Liu, Wenting Chen, Zhanjia Yang, Linlin Shen2180-2188
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three publicly available datasets, i.e. Morph, Ra FD and Celeb AMask-HQ, suggest that our approach demonstrate obvious improvement over state-of-the-art methods like Star GAN, SEAN and FUNIT. |
| Researcher Affiliation | Academia | Wenshuang Liu1,2,3, Wenting Chen1,2,3, Zhanjia Yang1,2,3, Linlin Shen1,2,3 1 Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University 2 Shenzhen Institute of Artiļ¬cial Intelligence & Robotics for Society 3 Guangdong Key Laboratory of Intelligent Information Processing {liuwenshuang2018, chenwenting2017, yangzhanjia2019}@email.szu.edu.cn, llshen@szu.edu.cn |
| Pseudocode | No | The paper describes the system framework and components but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper only provides a link to a video demo: '(https://youtu.be/DvIdmcR2LEc)', and does not explicitly state that the source code for the methodology is openly available or provide a link to a code repository. |
| Open Datasets | Yes | Our approach is extensively tested on three publicly available datasets, i.e. Morph (Ricanek and Tesafaye 2006), Ra FD (Langner et al. 2010) and Celeb AMask-HQ (Lee et al. 2020; Karras et al. 2017; Liu et al. 2015). |
| Dataset Splits | No | The paper specifies training and test set divisions for each dataset (e.g., 'We divide the images into a training set with 50020 images and a test set with 4,925 images' for Morph), but it does not explicitly state a separate validation dataset split. |
| Hardware Specification | Yes | We perform all traning runs on NVIDIA DGX with one Tesla V100 GPU using Pytorch 1.1.0 and cu DNN 7.4.2. |
| Software Dependencies | Yes | We perform all traning runs on NVIDIA DGX with one Tesla V100 GPU using Pytorch 1.1.0 and cu DNN 7.4.2. |
| Experiment Setup | Yes | Note that all the baselines are trained with the batch size of 4, the image size of 128 128 and the maximum iteration of 100,000. |