Learning Representations that Support Robust Transfer of Predictors

Last update: Dec 07, 2022

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Deep Learning TRM

Overview

Transfer Risk Minimization (TRM)

Code for Learning Representations that Support Robust Transfer of Predictors

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

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)

Owner

Yilun Xu

Hello!

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Learning Representations that Support Robust Transfer of Predictors

Related tags

Overview

Transfer Risk Minimization (TRM)

Prepare the Datasets

Preprocess the SceneCOCO dataset :

Running the Experiments

Owner

Yilun Xu

Distributed DataLoader For Pytorch Based On Ray

Tensorflow 2.x implementation of Vision-Transformer model

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YuNetのPythonでのONNX、TensorFlow-Lite推論サンプル

Pytorch implementation of AREL

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