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].
Adaptive GNN for Image Analysis and Editing
Authors: Lingyu Liang, LianWen Jin, Yong Xu
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show the effectiveness of the QIA-GNN, and provide new insights of GNN for image analysis and editing. |
| Researcher Affiliation | Academia | Lingyu Liang South China Univ. of Tech. EMAIL Lianwen Jin South China Univ. of Tech. EMAIL Yong Xu South China Univ. of Tech. Peng Cheng Laboratory EMAIL |
| Pseudocode | No | The paper does not contain 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 for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper does not provide specific names, links, DOIs, or formal citations for publicly available or open datasets used in the experiments. It refers to tasks like 'Face Relighting' and 'Low-Light Image Enhancement' without naming the datasets. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Typically, the parameters are set as α = 1.2 and ε = 0.0001. d(M) is spatially determined by different region, so that background, eyes and eyebrows are smoothed out, while the informative illumination in the facial region is preserved. ... Smoothness parameter d are controlled by M, so that d is large (typically d = 10) in V\S to produce illumination propagation, and d is small (typically d = 0.4) in S to preserve the significant illumination detail. |