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..
Learning Compact Semantic Information for Incomplete Multi-View Missing Multi-Label Classification
Authors: Jie Wen, Yadong Liu, Zhanyan Tang, Yuting He, Yulong Chen, Mu Li, Chengliang Liu
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple benchmarks validate our advantages and demonstrate strong compatibility with both missing and complete data. 4. Experiments 4.1. Experimental Settings 4.2. Experimental Results and Analysis |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518000 China 2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA. |
| Pseudocode | Yes | Algorithm 1 Training process of COME |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | Datasets: In line with previous works (Tan et al., 2018; Liu et al., 2023), we conduct experiments on five multi-view multi-label datasets, i.e., Corel5k (Duygulu et al., 2002), Pascal07 (Everingham et al., 2010), ESPGame (Von Ahn & Dabbish, 2004), IAPRTC12 (Grubinger et al., 2006), and Mirflickr (Huiskes & Lew, 2008). |
| Dataset Splits | Yes | (3) Dataset Splitting: Subsequently, 70% of the resulting samples are randomly selected as the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Algorithm 1 Training process of COME ... Initialization: Initialize the parameters of the model A and set hyper-parameters (λ1, λ2, β, and training epochs E) ... when the value of β is 0.1 and 1 for Corel5k and Pascal07 datasets, respectively, information compression and effective information reconstruction reach a balanced state, and the model achieves the optimal performance. |