Degradation Accordant Plug-and-Play for Low-Rank Tensor Completion
Authors: Yexun Hu, Tai-Xiang Jiang, Xi-Le Zhao
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on different types of tensor imaging data with the comparison with state-of-the-art methods, illustrating the effectiveness and the remarkable generalization ability of our method. Numerical experiments are conducted on various types of multidimensional images. Comparisons with state-of-the-art methods illustrate the excellent performance and generalization ability to different types of data of our method. |
| Researcher Affiliation | Academia | 1School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics 2School of Mathematical Sciences, University of Electronic Science and Technology of China |
| Pseudocode | No | The paper describes the ADMM algorithm and its subproblems in detail using mathematical equations and prose, but it does not provide a formally labeled pseudocode block or algorithm block. |
| Open Source Code | Yes | Can be found at https://github.com/TaiXiangJiang/. |
| Open Datasets | Yes | In this subsection, 6 color images of the size 512 512 3 are selected. Available at http://sipi.usc.edu/database/database.php. |
| Dataset Splits | No | The paper discusses 'random sampling with the sampling rates (SRs) 0.03 and 0.05' and 'different types of structural missing' for creating incomplete observations to evaluate the completion method. This describes how the evaluation data is generated or masked, not a specific training/validation/test split for model training or hyperparameter tuning in the traditional sense, especially as it uses pre-trained CNNs. |
| Hardware Specification | Yes | All experiments were conducted on the platform of Window 10 with an AMD Ryzen9 3950X CPU and RTX 2080Ti GPU and 32RAM. |
| Software Dependencies | No | The paper mentions 'Window 10' as the platform but does not provide specific version numbers for any key software components like programming languages (e.g., Python), deep learning frameworks (e.g., PyTorch, TensorFlow), or specific libraries used in the implementation. |
| Experiment Setup | No | The paper identifies four parameters (λ, β1, β2, and β3) and discusses their sensitivity. It states that when testing one parameter, 'the other three are fixed as default values,' but it does not specify what those default values are for the main experiments, nor does it provide other specific hyperparameters or training configurations. |