MiriamHu / ActiveBoundary

Active Decision Boundary Annotation with Deep Generative Models

Active Decision Boundary Annotation with Deep Generative Models

Miriam W. Huijser and Jan C. van Gemert
International Conference on Computer Vision 2017 (ICCV): spotlight presentation.

Paper: https://jvgemert.github.io/pub/huijserICCV17ActiveBoundAnnoGAN.pdf
arXiv preprint: https://arxiv.org/abs/1703.06971

We provide the code for the decision boundary annotation active learning algorithm. If you find my code useful for your research, please cite:

@article{huijser2017active,
  title={Active Decision Boundary Annotation with Deep Generative Models},
  author={Huijser, Miriam W and van Gemert, Jan C},
  booktitle={International Conference on Computer Vision ({ICCV})},
  year={2017}
}

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Installation

Clone this repository recursively because of the adapted ALI submodule:
git clone --recursive https://github.com/MiriamHu/ActiveBoundary.git

Then install requirements:

cd ActiveBoundary
pip install -r requirements.txt

Running the code

The code is compatible only with Python2.7 (python).
The training code can be run with the following command:

python train.py <query_strategy> [--iterations ITERATIONS] [--enable_gpu] [--oracle_type {line_labeler,noisy_line_labeler,human_line_labeler}] [--dataset {shoebag,mnist08,svhn08}] [--save_path SAVE_PATH] --percentage_labeled PERCENTAGE_LABELED] [--al_batch_size AL_BATCH_SIZE] 

<query_strategy> should be one of the following:

1. random - random sampling, also called "passive learning".
2. uncertainty - uncertainty sampling.
3. uncertainty-dense - uncertainty-dense sampling.
4. clustercentroids - 5-cluster centroids.

If --enable-gpu, the query lines (and points) are generated and saved as *.pdf in the save path.

For --oracle_type human_line_labeler, we require --enable-gpu. The user is shown an interface in which the decision boundary can be annotated.

For more (hyper)parameters please refer to options.py.

The first time train.py is run for a certain dataset, the required data (and model if gpu is enabled) is downloaded automatically.

Embedding

Any type of encoding-decoding network can be plugged into our system. See generative_model.py how to create your own class.

Our system currently uses the "Adversarially Learned Inference" model (Dumoulin et al., 2016). See https://ishmaelbelghazi.github.io/ALI/ for more information and the code.

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Datasets

We provide most encoded and non-encoded datasets from our paper:

• MNIST 0 vs. 8
• SVHN 0 vs. 8
• ShoeBag

To download a dataset either run train.py --dataset [mnist08, svhn08, shoebag] or run your own python script with the last lines of code in options.py.

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Please do not hesitate to contact me ([e-mail]) if you have any questions.
https://www.aiir.nl/