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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sense Beauty by Label Distribution Learning
Authors: Yi Ren, Xin Geng
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 Experiments: We do the experiment on the M2B dataset, and set λ to 20. To prove the accuracy, BDT algorithm is compared with the conventional pairwise ranking method Ranking SVM following the settings in [Nguyen et al., 2012]. We measure the performance by comparing the results with the pairwise preferences obtained from k-wise comparisons. |
| Researcher Affiliation | Academia | Yi Ren, Xin Geng MOE Key Laboratory of Computer Network and Information Integration, School of computer Science and Engineering, Southeast University, Nanjing, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Structural Label Distribution Learning(SLDL) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology (BDT or SLDL) is publicly available. |
| Open Datasets | Yes | In the experiments, we use the SCUT-FBP dataset[Xie et al., 2015] and the Multi-Modality Beauty (M2B) dataset [Nguyen et al., 2012], which are just fit to the two conditions mentioned before, the former contains the whole ratings of each image, and the latter contains the k-wise comparisons. |
| Dataset Splits | Yes | Ten-fold cross validation is conducted. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using "BFGS" as an optimization algorithm and specific feature descriptors like "LBP", "HOG", and "Gabor filter". It also mentions "SVR" and "k-NN" as baseline methods. However, it does not provide specific version numbers for any software libraries, frameworks, or solvers used (e.g., Python version, TensorFlow/PyTorch version, specific library versions for SVR or k-NN). |
| Experiment Setup | Yes | The regularization parameter in simplified DFAT network is set to 0.005. SVR is set with a linear kernel. The k in k-NN is set to 1, and the distance is computed by the Euclidean distance. SLDL is set with a linear kernel and C is 400. We do the experiment on the M2B dataset, and set λ to 20. The features of the images are extracted by three popular descriptors, i.e., LPB [Ojala et al., 2002] with a cell size of 64 × 64 pixels; HOG [Dalal and Triggs, 2005] with a cell size of 32 × 32 pixels; Gabor filter [Jain and Farrokhnia, 1991] with 2 scales and 4 orientations. Since the features we extracted are high-dimensional, we use PCA to reduce the dimensionality to 300. |