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 Robust Multi-view Representation Using Dual-masked VAEs
Authors: Jiedong Wang, Kai Guo, Peng Hu, Xi Peng, Hao Wang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four multi-view datasets show the effectiveness of the proposed method and its superior performance in comparison to baselines. We conduct experiments on four publicly available multi-view datasets. The statistics of each dataset are shown in Table 1. |
| Researcher Affiliation | Academia | 1College of Computer Science, Sichuan University, China 2National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, China |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical formulations, but it does not contain any explicitly labeled pseudocode or algorithm blocks. For example, Section 3 'Methodology' and its subsections describe the process in detail but without a structured algorithm format. |
| Open Source Code | Yes | The code is available at https://github.com/XLearning-SCU/ 2025-IJCAI-Dual VAE. |
| Open Datasets | Yes | To evaluate the proposed model, we conduct experiments on four publicly available multi-view datasets. The statistics of each dataset are shown in Table 1. COIL-20 [Nene et al., 1996b] COIL-100 [Nene et al., 1996a] E-MNIST [Liu and Tuzel, 2016] Poly MNIST [Palumbo et al., 2023] |
| Dataset Splits | No | The paper defines View-Missing Ratio (VMR) and Sample-Noise Ratio (SNR) for evaluating the model's robustness under different conditions, and states that these ratios are varied from 0% to 90%. However, it does not provide specific training/validation/test splits (e.g., 80/10/10 split percentages or sample counts) for the model's primary training process. The experimental results are based on clustering and classification tasks on 'learnt representations', implying a training phase, but the split details for this phase are not explicitly provided. |
| Hardware Specification | Yes | We deployed the proposed model and baselines using Pytorch 2.3.1 and ran experiments on NVIDIA TITAN GPUs with 24GB of memory in Ubuntu 20.04.1 LTS. |
| Software Dependencies | Yes | We deployed the proposed model and baselines using Pytorch 2.3.1 and ran experiments on NVIDIA TITAN GPUs with 24GB of memory in Ubuntu 20.04.1 LTS. |
| Experiment Setup | Yes | We utilize Adam optimizer with weight decay and set training epochs as 200. Moreover, we set the initial learning rate to 1e-4 and adopt cosine annealing during the training. Following [He et al., 2022; Ke et al., 2024], the default patch-mask ratio is 0.7. We then initially set view-mask ratio as 0.3. Both the dimensions of view-consistent representation and view-specific representation are 10. |