Active Learning under Label Shift
This repository is the official implementation of Active Learning Under Label Shift.
Requirements
To install requirements:
pip install -r requirements.txt
We strongly recommend working in a Docker environment. You can spin up a container for this project using:
NV_GPU=$GPU nvidia-docker run -it -v /home/$USER/alls:/content --name $NAME --shm-size=10g nvcr.io/nvidia/pytorch:20.03-py3
While MNIST, CIFAR, and CIFAR100 datasets and managed and installed by torchvision, NABirds requires manual installation.
The NABirds dataset can be downloaded here.
The dataset should be unzipped and installed under /content/data/.
Edit alsa/config.py to reflect the new project root directory and enter Comet.ml
credentials for monitoring experiments.
Training
Experiments (training and evaluation) are run through python3 -m alsa.main.replicate.
Command flags are described under alsa/main/args.py. For examples of how to
batch multiple active learning seeds and experiments, see example_exps for sample
scripts to run experiments, including for replicating results from the original paper.
Model and environment hyperparameters are described in the original paper's appendix
for completeness.
Evaluation
Experiment results are automatically uploaded to Comet.ml where they can be downloaded as CSVs. See their documentation for downloading assets.
Pre-trained Models
No pretrained models are necessary.
Contributing
This code is released under the MIT License.
Copyright 2020 Redacted
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.