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..
Clustered-patch Element Connection for Few-shot Learning
Authors: Jinxiang Lai, Siqian Yang, Junhong Zhou, Wenlong Wu, Xiaochen Chen, Jun Liu, Bin-Bin Gao, Chengjie Wang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our CECNet outperforms the state-of-the-art methods on classification benchmark. |
| Researcher Affiliation | Collaboration | Jinxiang Lai1 , Siqian Yang1 , Junhong Zhou2 , Wenlong Wu1 , Xiaochen Chen1 , Jun Liu1 , Bin-Bin Gao 1 , Chengjie Wang 1,3 1Tencent Youtu Lab, China 2Southern University of Science and Technology, China 3Shanghai Jiao Tong University, China |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Other implementation details can be found in our public code. |
| Open Datasets | Yes | The two popular FSL classification benchmark datasets are mini Image Net and tiered Image Net, where detailed introductions are presented in APPENDIX. |
| Dataset Splits | Yes | In the recent investigations[Hou et al., 2019; Snell et al., 2017], the source dataset is divided into three category-disjoint parts: training set Xtrain, validation set Xval and test set Xtest. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions 'pytorch code' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | According to Tab. 4, the hyperparameter λ is set to 1.0 and 2.0 for Res Net-12 and WRN-28, respectively. Other implementation details can be found in our public code. |