This repository supplies an implementation of a the supervised domain adaptation method Domain Adaptation using Graph Embedding (DAGE).

We additionally provide implementations of the following baseline transfer learning and domain adaptation methods:

• Fine tuning using gradual unfreeze
• CCSA
• d-SNE

$ conda env create --file environment.yml
$ conda activate dage

$ ./scripts/get_office.sh
$ ./scripts/get_digits.sh

run.py is the entry-point for running the implemented methods. To retreive a list of valid arguments, use python run.py --help.

A number of ready-to-run scripts are supplied (found in the scripts folder), with which one can test different methods and configurations.

An example which tunes a model on source data, and tests on target data is

$ ./scripts/office31_tune_source.sh

Running DAGE of Office31 with tuned hyperparameters is acheived by using.

$ /scripts/office31_dage_lda_tuned_vgg16.sh

A separate python entry-point hypersearch.py can be used to perform a hyper-parameter search using Bayesian Optimisation.

A number of notebooks are supplied, in which one can visualise the Office-31 data, see the results of our experiments, and the conducted hyper-parameter search.

• Lukas Hedegaard - https://github.com/lukashedegaard
• Omar Ali Sheikh-Omar - https://github.com/sheikhomar