Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Low-Light Face Super-resolution via Illumination, Structure, and Texture Associated Representation
Authors: Chenyang Wang, Junjun Jiang, Kui Jiang, Xianming Liu
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 17,280 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,139 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that the proposed joint optimization framework achieves significant improvements in reconstruction quality and perceptual quality over existing two-stage sequential solutions. |
| Researcher Affiliation | Academia | Chenyang Wang, Junjun Jiang*, Kui Jiang, Xianming Liu School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/wcy-cs/IC-FSRDENet. |
| Open Datasets | Yes | We conduct experiments on commonly-used high quality face dataset Celeb AMask-HQ (Yu et al. 2018). |
| Dataset Splits | Yes | From it, we randomly choose 3050 face images for training, and 600 for validation, and another 300 for testing. |
| Hardware Specification | Yes | The experiments are implemented on Py Torch with one NVIDIA 3090 GPU. |
| Software Dependencies | No | The experiments are implemented on Py Torch with one NVIDIA 3090 GPU. This only mentions “Py Torch” without a specific version number and does not list other software dependencies with versions. |
| Experiment Setup | Yes | In IC-FSRNet, L is set as 14. IC-FSRNet and DENet are trained successively and individually. The optimizer is Adam with β1=0.9, β2=0.99, and ϵ=1e-8. The learning rate is 1e-4 for both two networks. Towards the DENet, the backbone is UNet and other settings follow (Saharia et al. 2021). |