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README.md

MMA - Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels

Code for the paper: "Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels" by Shuang Song, David Berthelot, and Afshin Rostamizadeh.

This is not an officially supported Google product.

Setup

Install dependencies

sudo apt install python3-dev python3-virtualenv python3-tk imagemagick
virtualenv -p python3 --system-site-packages env3
. env3/bin/activate
pip install -r requirements.txt

Install datasets

export ML_DATA="path to where you want the datasets saved"
# Download datasets
CUDA_VISIBLE_DEVICES= ./scripts/create_datasets.py

Running

We have hard-coded the parameters (batch for AL and number of iterations between each querying) used in the paper in mixmatch_lineargrow.py. The parameters are documented and can be changed there.

To do the experiment on CIFAR-10 with diff as the uncertainty measurement on two augmentations of samples and no diversification method, i.e.,training mixmatch with 32 filters on CIFAR-10 shuffled with seed=1, starting from 250 randomly selected samples, querying 50 each time until 4000 labelled samples with diff.aug-direct:

CUDA_VISIBLE_DEVICES=0 python mixmatch_lineargrow.py --filters=32 --w_match=75 --beta=0.75 --dataset=cifar10.1@250_train50000 --grow_size=50 --grow_by=diff2.aug-direct

Monitoring training progress

You can point tensorboard to the training folder (by default it is --train_dir=./MMA_exp) to monitor the training process:

tensorboard.sh --port 6007 --logdir MMA_exp

Citing this work

@misc{song2019combining,
      title={Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels},
      author={Shuang Song and David Berthelot and Afshin Rostamizadeh},
      year={2019},
      eprint={1912.00594},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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