Clone this repo, then:
pip install -r requirements.txt
experiment_folder: experiments will be saved at<experiment_folder>/<experiment_name>dataset_folder: datasets will be downloaded here. For example,<dataset_folder>/mnistmand<dataset_folder>/office31conda_envandslurm_folderare for running jobs on slurm. (I haven't uploaded the slurm-related code yet.)
python main.py --experiment_name dann_experiment --dataset mnist \
--src_domains mnist --target_domains mnistm --adapter DANNConfig \
--download_datasets --start_with_pretrained
python main.py --experiment_name mcc_experiment --dataset officehome \
--src_domains art --target_domains real --adapter MCCConfig \
--download_datasets --start_with_pretrained
python main.py --experiment_name bnm_experiment --dataset office31 \
--src_domains dslr --target_domains amazon --adapter BNMConfig \
--batch_size 32 --max_epochs 500 --patience 15 \
--validation_interval 5 --num_workers 4 --num_trials 100 --n_startup_trials 100 \
--validator entropy_diversity --optimizer_name Adam \
--download_datasets --start_with_pretrained
Some names in the code don't match the names in the paper. It would be good to change the names in the code, but I'm going to delay doing that, in case I have to rerun experiments and combine new dataframes with existing saved dataframes.
Here are the main differences between code and paper:
| Code | Paper |
|---|---|
--validator entropy_diversity |
Information Maximization (IM) validator |
--adapter TEConfig |
MinEnt algorithm |
--adapter TEDConfig |
IM algorithm |
The notebooks folder currently contains:
If you'd like to cite the paper, paste this into your latex bib file:
@misc{musgrave2021unsupervised,
title={Unsupervised Domain Adaptation: A Reality Check},
author={Kevin Musgrave and Serge Belongie and Ser-Nam Lim},
year={2021},
eprint={2111.15672},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Looking for A Metric Learning Reality Check?
Checkout the metric-learning branch.
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