Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling
Authors: Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao4602-4609
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic data validate our theory, and results on image steganography show that our method outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | Chao Li,1 Mohammad Emtiyaz Khan,1 Zhun Sun,1 Gang Niu,1 Bo Han,1 Shengli Xie,2, Qibin Zhao1, 1RIKEN Center for Advanced Intelligence Project (AIP), Japan 2School of Automation, Guangdong University of Technology, China |
| Pseudocode | Yes | Algorithm 1 Reshuffled-TD |
| Open Source Code | Yes | Supplementary materials are available at: http://qibinzhao.github.io. |
| Open Datasets | Yes | The datasets we used in the experiment include texture (DTD), natural (LIVE and FIVEK (Bychkovsky et al. 2011)), cartoon (CART. (Royer et al. 2017)) and fingerprint (FVC (Maltoni et al. 2009)) datasets. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits with specific percentages or sample counts. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies or their version numbers (e.g., Python, PyTorch, specific libraries) are mentioned in the paper. |
| Experiment Setup | Yes | We generate data by using N square matrices A i Rn n, i [N]. Each A i is generated by multiplying two random semiorthonormal matrices with rank r... We fix the size of the components n = 100, the number of the components N = 10 and set the rank of each component by r = 1, . . . , 4. Then, we add the zero-mean i.i.d. Gaussian noise to the data, and the variance of the noise is controlled by the signal to noise ratio (SNR). During the concealing phase, we consider each channel of the secret image as one component, and they are randomly reshuffled. To estimate the number of components by Reshuffled-TD, we compare the norm of the recovered components with a threshold (we choose η = 0.1 for numerical consideration). |