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 | Conference PDF | Archive PDF | Plain Text | 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. |