Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning
Authors: Jiawei Yao, Qi Qian, Juhua Hu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |