Label distribution learning with label-specific features
Authors: Tingting Ren, Xiuyi Jia, Weiwei Li, Lei Chen, Zechao Li
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on several real-world data sets validate the effectiveness of our method. (Abstract) |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanjing University of Science and Technology, China 2Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, China 3State Key Laboratory for Novel Software Technology, Nanjing University, China 4College of Astronautics, Nanjing University of Aeronautics and Astronautics, China |
| Pseudocode | Yes | Algorithm 1: The LDLSF Framework (Section 3.2) |
| Open Source Code | No | The paper does not contain any statement about releasing its code, nor any links to a code repository. The phrase 'All the codes are shared by original authors' refers to competitor algorithms, not their own. |
| Open Datasets | Yes | We execute our experiments on five label distribution data sets, including two facial expression data sets s-JAFFE and SBU 3DFE [Geng, 2016], and three image sentiment data sets, i.e., Emotion6 [Peng et al., 2015], Flickr LDL and Twitter LDL [Yang et al., 2017b]. (Section 4.1) |
| Dataset Splits | Yes | On each data set, ten times ten-folds cross-validation is conducted and the mean value and standard deviation of each evaluation criterion is recorded. (Section 4.4) tune the trade-off parameters from 10{ 4, 3, ,2,3} for LDL-SCL and tune the parameters from 2{10, 9, ,9,10} for LLSF-LDL using ten-fold cross-validation. (Section 4.3) |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions methods like ADMM, L-BFGS, and PCA but does not provide specific version numbers for any software libraries or dependencies used in their implementation. |
| Experiment Setup | Yes | In LDLSF, the parameters λ1, λ2 and λ3 are selected from 10{ 6, 5, , 2, 1}, respectively, and ρ is simply set as 10 3. Besides, Q is initialized by the identity matrix. Both W and M are diagonal matrices in which all diagonal elements are 0.5. The initialization of other variables is all-zero. (Section 4.3) |