Awesome
DIRT-T
Implementation of A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018).
Dependencies
numpy==1.14.1
scikit_image==0.13.1
scipy==1.0.0
tensorflow_gpu==1.6.0
tensorbayes==0.4.0
Download Data
Download scripts for MNIST and SVHN provided in ./data/.
Run code
Run VADA
python run_dirtt.py --datadir data --run 0 --src mnist --trg svhn --dirt 0
Run DIRT-T (pre-condition: run VADA first)
python run_dirtt.py --datadir data --run 0 --src mnist --trg svhn --dirt 5000
Tensorboard logs will be saved to ./log/ by default.
VADA and DIRT-T Performance
<img src="assets/vada_dirtt.png" width="500"/>Test run of a single VADA run on MNIST -> SVHN, and using the final VADA model as initialization for 4 separate DIRT-T runs. DIRT-T has higher variance but, on expectation, improves upon VADA.
Tensorboard Visualization
