Instance-Level Facial Attributes Transfer with Geometry-Aware Flow
Authors: Weidong Yin, Ziwei Liu, Chen Change Loy9111-9118
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations validate the capability of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. In this section, we comprehensively evaluate our approach on different benchmarks with dedicated metrics. |
| Researcher Affiliation | Academia | Weidong Yin University of British Columbia wdyin@cs.ubc.ca Ziwei Liu Chinese University of Hong Kong zwliu@ie.cuhk.edu.hk Chen Change Loy Nanyang Technological University ccloy@ntu.edu.sg |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a project page link (http://mmlab.ie.cuhk.edu.hk/projects/ attribute-transfer/) but does not provide a direct link to a source-code repository (e.g., GitHub, GitLab, Bitbucket) nor an explicit statement of code release. |
| Open Datasets | Yes | Extensive evaluations on Celeb A (Liu et al. 2015) and Celeb A-HQ (Karras et al. 2017) datasets validate the effectiveness of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. |
| Dataset Splits | Yes | We use the standard training, validation and test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components and architectures like Adam optimizer, Pix2Pix HD, ResNet-18, LSGAN, Patch GAN, and UNet, but does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | Input image values are normalized to [ 1, 1]. All models are trained using Adam (Kingma and Ba 2014) optimizer with a base learning rate of 0.002, and a batch size of 8. We perform data augmentation by random horizontal flipping with a probability of 0.5. |