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].
Colorization by Patch-Based Local Low-Rank Matrix Completion
Authors: Quanming Yao, T. Kwok James
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a number of benchmark images demonstrate that the proposed method outperforms existing approaches. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong EMAIL |
| Pseudocode | Yes | The proposed procedure, which will be called Patchbased Local Low-Rank colorization (Pa LLR) in the sequel, is shown in Algorithm 2. ... Fast Inv(R) using Algorithm 3... |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only mentions that 'Codes for LCC and GLR are obtained from their authors', referring to baseline methods, not their own. |
| Open Datasets | Yes | Experiments are performed on eight color images from the Berkeley segmentation data set (Figure 3). |
| Dataset Splits | No | The paper states 'Varying numbers of pixels (1% 10%) are randomly sampled from the color image as observed labels input to the colorization algorithm', but does not provide specific training/validation/test splits for the images in the dataset. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'MATLAB notation' but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We fix the patch size r to 16, and use k = 50 patches in each Pa LLR group. ...For ℓ2,... we fix the proportionality constant to 5, and µ = 0.16. |