Transfer Risk Minimization (TRM)
Code for Learning Representations that Support Robust Transfer of Predictors, Yilun Xu, Tommi Jaakkola
Prepare the Datasets
Preprocess the SceneCOCO dataset :
# preprocess COCO
python coco.py
# preprocess Places
python places.py
# generate SceceCOCO dataset
python cocoplaces.py
Running the Experiments
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Datasets:
- Synthetic datasets for controlled experiments: ColorMNIST / SceneCOCO
- Real-world datasets: PACS / Office-Home
python -m domainbed.scripts.train --data_dir {root} --algorithm {alg} \
--dataset {dataset} --trial_seed {t_seed} --epochs {epochs} (--resnet50)
root: root directory for the data
alg: ERM, VREx, IRM, GroupDRO, Fish, MLDG, TRM
t_seed: seed for data splitting
dataset: PACS or OfficeHome or ColoredMNIST or SceneCOCO
resnet50: use ResNet50 (default: ResNet18)
epochs: training epochs
This implementation is based on / inspired by:
-
https://github.com/facebookresearch/DomainBed (code structure).
-
https://github.com/Faruk-Ahmed/predictive_group_invariance (data generation)
