/Cost-Effective-Active-Learning-for-Deep-Image-Classification

Cost-Effective Active Learning for Deep Image Classification, IEEE TCSVT, 2016

Primary LanguageJupyter Notebook

CEAL: Cost-Effective Active Learning for Deep Image Classification

Unofficial Pytorch Implementation of CEAL

  • This is not an official implementation of CEAL!
  • I've only tested this code with CIFAR10 and modified CUB200 dataset because of limitation of time and GPU resources I've got.
  • Feel free to leave me an issue or make a pull request!

Files

  • CEAL.ipynb: CEAL on CIFAR10 dataset
  • CEAL_CUB.ipynb: CEAL on modified CUB200 dataset
  • model.py: defines model

modified CUB200

  • Because of limited GPU resource, I modified original CUB200 dataset with 200 class into 20 classes.
  • First, sort the data by class name. (A-Z)
  • And select the top 20 classes for training and testing.
  • I used train/test split provided by TensorFlow.

Original Paper

Wang, Keze, et al. "Cost-effective active learning for deep image classification." IEEE Transactions on Circuits and Systems for Video Technology 27.12 (2016): 2591-2600.