Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Residual Attribute Attention Network for Face Image Super-Resolution
Authors: Jingwei Xin, Nannan Wang, Xinbo Gao, Jie Li9054-9061
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark database illustrate that RAAN significantly outperforms state-of-the-arts for very low-resolution face SR problem, both quantitatively and qualitatively. |
| Researcher Affiliation | Academia | Jingwei Xin, Nannan Wang, Xinbo Gao, Jie Li State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian University, Xi an 710071, China State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi an 710071, China Corresponding author: Nannan Wang (EMAIL) |
| Pseudocode | No | The paper describes the network architecture and mathematical formulations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of open-source code or a link to a code repository. |
| Open Datasets | Yes | We conduct extensive experiments on celeb A dataset (Liu, Luo, and Wang 2015). We use the ๏ฌrst 18000 images for training, and the following 100 images for testing. |
| Dataset Splits | No | We use the ๏ฌrst 18000 images for training, and the following 100 images for testing. |
| Hardware Specification | Yes | Training a basic RAAN on celeb A dataset generally takes 5 hours with one Titan X Pascal GPU. |
| Software Dependencies | No | We implement our moel using the pytorch environment. |
| Experiment Setup | Yes | Adam with an initial learning rate of 3 10 4 are used in our model. The batch size is set to 16. We empirically set "lambda" = 1 and "gamma" = 0.01. |