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..
Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning
Authors: Jiawei Yao, Qi Qian, Juhua Hu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method consistently outperforms existing baselines across a broad set of datasets in visual multiple clustering tasks. |
| Researcher Affiliation | Collaboration | Jiawei Yao1 Qi Qian2 Juhua Hu1 1 School of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA 2 Zoom Video Communications |
| Pseudocode | No | The paper does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/Alexander-Yao/Multi-Sub. |
| Open Datasets | Yes | we evaluate the proposed method on almost all publicly available visual datasets commonly used in multiple clustering tasks Yu et al. [2024], including Stanford Cars Yao et al. [2024], Card Yao et al. [2023], CMUface Günnemann et al. [2014], Flowers Yao et al. [2024], Fruit Hu et al. [2017] and Fruit360 Yao et al. [2023]. Additionally, we created a multiple clustering dataset from CIFAR-10 Krizhevsky et al. [2009] |
| Dataset Splits | No | The paper mentions training for 1000 epochs and tuning hyperparameters, but does not explicitly provide training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | The experiments are performed on four NVIDIA Ge Force RTX 2080 Ti GPUs. |
| Software Dependencies | No | The paper mentions using CLIP and GPT-4, and the Adam optimizer, but does not provide specific version numbers for any software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For each user s preference, we train the model for 1000 epochs using Adam optimizer with a momentum of 0.9. We tune all the hyper-parameters based on the loss score of Multi-Sub, where the learning rate is selected from {1e-1,5e-2,1e-2,5e-3,1e-3,5e-4}, weight decay is chosen from {5e-4,1e-4,5e-5,1e-5, 0} for all the experiments. |