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..
Multi-Level Cross-Modal Alignment for Image Clustering
Authors: Liping Qiu, Qin Zhang, Xiaojun Chen, Shaotian Cai
AAAI 2024 | Venue PDF | 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 EMAIL, EMAIL, EMAIL |
| 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. |