Deep InterBoost Networks for Small-sample Image Classification

Code release for Deep InterBoost Networks for Small-sample Image Classification (NEUROCOMPUTING 2020) DOI

Changelog

• 2020/11/20 upload the code.

Dataset

• Subset of LabelMe (train :100 for each class, validation:10 for each class, test:100 for each class)

Requirements

• python 2.7
• keras 2.0.6
• tensorflow-gpu 1.2.1

Training

• Download datasets and extract the deep features.
• Train: interBoost.py
• Description : Keras LabelMe Training with interboost.

Citation

If you find this paper useful in your research, please consider citing:

@article{li2020deep,
  title={Deep InterBoost Networks for Small-sample Image Classification},
  author={Li, Xiaoxu and Chang, Dongliang and Ma, Zhanyu and Tan, Zheng-Hua and Xue, Jing-Hao and Cao, Jie and Guo, Jun},
  journal={Neurocomputing},
  year={2020},
  publisher={Elsevier}
}

Contact

Thanks for your attention! If you have any suggestion or question, you can leave a message here or contact us directly:

• mazhanyu@bupt.edu.cn
• xiaoxulilut@gmail.com
• changdongliang@bupt.edu.cn