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..
Deep Adversarial Multi-view Clustering Network
Authors: Zhaoyang Li, Qianqian Wang, Zhiqiang Tao, Quanxue Gao, Zhaohua Yang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several real-world datasets demonstrate the proposed method outperforms the state-of art methods. 4 Experiments |
| Researcher Affiliation | Academia | 1State Key Laboratory of Integrated Services Networks, Xidian University, Xi an 710071, China. 2Department of Electrical and Computer Engineering, Northeastern University, USA. 3School of Instrumentation Science and Opto-electronics Engineering, Beihang University, China. |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not provide a link to open-source code or explicitly state that the code is available. |
| Open Datasets | Yes | Handwritten numerals (HW) dataset [Asuncion and Newman, 2007] is composed of 2,000 data points from 0 to 9 ten digit classes and each class has 200 data points. BDGP [Cai et al., 2012] is a twoview dataset including two different modalities, i.e., visual and textual data. The Columbia Consumer Video (CCV) dataset [Jiang et al., 2011] contains 9,317 You Tube videos with 20 diverse semantic categories. MNIST is a widely-used benchmark dataset consisting of handwritten digit images with 28 28 pixels. In our experiment, we employ its two-view version (70, 000 samples) provided by [Shang et al., 2017] |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | We run all the experiments on the platform of Ubuntu Linux 16.04 with NVIDIA Titan Xp Graphics Processing Units (GPUs) and 32 GB memory size. |
| Software Dependencies | No | The paper mentions "Py Torch" and "Adam optimizer" but does not specify version numbers for these software components. |
| Experiment Setup | Yes | We use Adam [Kingma and Ba, 2014] optimizer with default parameter setting to train our model and fix the learning rate as 0.0001. We conduct 30 epochs for each training step. |