Label Enhancement via Joint Implicit Representation Clustering
Authors: Yunan Lu, Weiwei Li, Xiuyi Jia
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments validate our proposal. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanjing University of Science and Technology, China 2College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China luyn@njust.edu.cn, liweiwei@nuaa.edu.cn, jiaxy@njust.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | Yes | We select six representative real-world LDL datasets from different tasks respectively, and their brief descriptions are shown in Table 1. For Emotion6 and Twitter-LDL, we extract a 168-dimensional feature vector for each instance [Ren et al., 2019]. Besides, we use min-max normalization to preprocess the feature vectors for all datasets to accelerate the convergence. Datasets include SBU-3DFE [Geng, 2016], Emotion6 [Peng et al., 2015], Twitter-LDL [Yang et al., 2017], Movie [Geng, 2016], Scene [Geng et al., 2022], Human Gene [Geng, 2016]. |
| Dataset Splits | No | The paper states, 'we first randomly dividing dataset (70% for training and 30% for testing)', but does not explicitly provide details for a validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'Adam [Kingma and Ba, 2015] is adopted as the optimizer' and 'Res Net-18 [He et al., 2016]' as a backbone neural network, but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For our JRC and LEIC, k is set to m+1, the dimension of the joint implicit representation is set to 64, λ is selected from {1, 2, , 10}, neural networks f are modeled as linear functions for simplicity, and Adam [Kingma and Ba, 2015] is adopted as the optimizer. |