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..
Robust Contrastive Multi-view Clustering against Dual Noisy Correspondence
Authors: Ruiming Guo, Mouxing Yang, Yijie Lin, Xi Peng, Peng Hu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five widely-used multi-view benchmarks, in comparison with eight competitive multi-view clustering methods, verify the effectiveness of our method in addressing the DNC problem. |
| Researcher Affiliation | Academia | Ruiming Guo1 , Mouxing Yang1 , Yijie Lin1, Xi Peng1,2, Peng Hu1 1College of Computer Science, Sichuan University, China 2State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, China |
| Pseudocode | No | The paper does not contain a pseudocode block or clearly labeled algorithm. |
| Open Source Code | Yes | The code is available at https://github.com/XLearning-SCU/2024-NeurIPS-CANDY. |
| Open Datasets | Yes | The experiments are carried out on the following five widely-used multi-view learning datasets. Scene-15 [42]... Caltech-101 [43]... Land Use-21 [45]... Reuters [47]... NUS-WIDE [49]. |
| Dataset Splits | No | The paper does not specify explicit train/validation/test dataset splits. It states: 'Since Mv C requires training and clustering on the same dataset, we conduct the view realignment strategy on the learned representation by following the PVP studies [20, 21].' |
| Hardware Specification | Yes | All evaluations are conducted on Ubuntu 20.04 OS with NVIDIA 3090 GPUs. |
| Software Dependencies | Yes | In the experiment, CANDY is implemented with PyTorch 2.1.2 |
| Experiment Setup | Yes | the model is optimized with the Adam [41] optimizer with a learning rate of 0.002 across all experiments, with a batch size fixed to 1024. ... The scale parameter σ in Eq. 3 is fixed as 0.07 across all experiments. ... η being a denoising hyper-parameter fixed as 0.2 in our experiments. ... λ is fixed as 0.2 in our experiments. ... for the first 20 epochs of training. |