Multi-Level Cross-Modal Alignment for Image Clustering
Authors: Liping Qiu, Qin Zhang, Xiaojun Chen, Shaotian Cai
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on five benchmark datasets clearly show the superiority of our new method. |
| Researcher Affiliation | Academia | Liping Qiu*, Qin Zhang*, Xiaojun Chen , Shaotian Cai Shenzhen University, Shenzhen, China qiuliping2021@email.szu.edu.cn, {qinzhang, xjchen}@szu.edu.cn, cai.st@foxmail.com |
| Pseudocode | No | The paper describes methods in text and figures but does not provide a formal pseudocode block or algorithm section. |
| Open Source Code | No | The paper does not explicitly state that open-source code is provided, nor does it include a link to a code repository. |
| Open Datasets | Yes | We used the following five benchmark datasets in our experiment: STL10 (Coates, Ng, and Lee 2011), Cifar10 (Krizhevsky 2009), Cifar100-20 (Krizhevsky 2009), Image Net Dogs (Chang et al. 2017b) and Tiny-Image Net (Le and Yang 2015). |
| Dataset Splits | No | The paper mentions the use of benchmark datasets and repeated training, but it does not specify explicit train/validation/test split percentages or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper discusses trade-off parameters and their sensitivity, as well as some hyperparameter names like τia, τpa, ρu, γr, γh. However, it does not provide a complete and specific list of all hyperparameters and system-level training settings (e.g., learning rate, batch size, optimizer) used to obtain the main experimental results, making full reproduction challenging. |