domain-invariant-learning

We extend current unsupervised domain adaptation (especially domain invariant representation learning) to solve huge covariate shift betwween source and target.
python 3.9.7

algo/

implementations of domain invariant learning algo. --algo_name can switch them (so far infeasible dan_algo.py in this manner).

file name	note
dann_algo.py	DANNs algo https://arxiv.org/pdf/1505.07818
coral_algo.py	CoRAL algo https://arxiv.org/abs/1607.01719
dan_algo.py	DAN algo https://arxiv.org/abs/1502.02791
dann2D_algo.py	see our paper(TODO: Attach in the near future)
supervised_algo.py	supervised deep learning boilerplate for comparison test

experiments/

implementations of experiment workflow (data load, preprocess, init NN, training, evaluation).

dir name	data	execution
make_moons	https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html	python -m domain-invariant-learning.experiments.make_moons.experiment
ecodataset	https://vs.inf.ethz.ch/res/show.html?what=eco-data	git clone https://github.com/oh-yu/deep_occupancy_detection/tree/feature/JSAI run all cells of 01.ipynb - 05.ipynb python -m domain-invariant-learning.experiments.ecodataset_synthetic.experiment
ecodataset_synthetic	see experiment.py logic	git clone https://github.com/oh-yu/deep_occupancy_detection/tree/feature/JSAI run all cells of 01.ipynb - 05.ipynb python -m domain-invariant-learning.experiments.ecodataset_synthetic.experiment
HHAR	https://archive.ics.uci.edu/dataset/344/heterogeneity+activity+recognition	download data python -m domain-invariant-learning.experiments.HHAR.experiment
MNIST	https://github.com/mashaan14/MNIST-M/tree/main	download data python -m domain-invariant-learning.experiments.MNIST.experiment

networks/

implementations of networks which include layers, fit method, predict method, predict_proba method. Domain Invariant Laerning and Without Adapt and Train on Target related free params should be set here.

file name	note
dann.py	Figure 4: from https://arxiv.org/pdf/1505.07818
codats.py	Figure 3: from https://arxiv.org/pdf/2005.10996
danns_2d.py	see our paper(TODO: Attach in the near future)
isih-DA.py	Algorythm 1 from https://www.jstage.jst.go.jp/article/tjsai/39/5/39_39-5_E-O41/_article/-char/ja/

utils/

Definition of generic functions to be called in multiple locations within the above dir structure.