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..
Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis
Authors: Quanxue Gao, Huanhuan Lian, Qianqian Wang, Gan Sun3938-3945
AAAI 2020 | 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-the-art methods. |
| Researcher Affiliation | Academia | 1State Key Laboratory of Integrated Services Networks, Xidian University, Xi an 710071, China. 2State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China. |
| Pseudocode | Yes | Algorithm 1 CMSC-DCCA |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | Datasets Settings: The used datasets in our experiments include: 1) FRGC Dataset (Yang, Parikh, and Batra 2016); 2) Fashion-MNIST Dataset (Xiao, Rasul, and Vollgraf 2017); 3) YTF Dataset (Wolf, Hassner, and Maoz 2011); 4) COIL-20 Dataset |
| Dataset Splits | No | The paper mentions 'train' steps but does not provide specific details on dataset splits (e.g., percentages or exact sample counts) for training, validation, or test sets. |
| Hardware Specification | Yes | NVIDIA Titan Xp Graphics Processing Units (GPUs) and 64 GB memory size. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | Yes | Implementation details: In our model, we use the four-layer encoders including three convolution encoding layers and a fully connected layer, and the corresponding decoders consist of a fully connected layer and three deconvolution decoding layers. More specific settings are given in Table 1. ... We set the learning-rate to 0.001. ... We set 10000 epochs to train the entire network |