sghoshjr/domain-adversarial-neural-network

Implementation of Domain Adversarial Neural Network in Tensorflow

Domain Adversarial Neural Network in Tensorflow

Implementation of Domain Adversarial Neural Network in Tensorflow.

Recreates the MNIST-to-MNIST-M Experiment.

Tested with tensorflow-gpu==2.0.0 and python 3.7.4.

MNIST to MNIST-M Experiment

Generating MNIST-M Dataset

Adapted from @pumpikano

To generate the MNIST-M Dataset, you need to download the BSDS500, and place it in ./Datasets/BSR_bsds500.tgz. Run the create_mnistm.py script.

Alternatively, the script create_mnistm.py will give you the option to download the dataset, if it is not found in the directory.

python create_mnistm.py

This should generate the ./Datasets/MNIST_M/mnistm.h5 file.

The dataset is also available here : mnistm.h5

Training

Run the DANN.py script.

python DANN.py

Uncomment the #train('source', 5) to use Source-only Training

Results

Note: The architecture and hyper-parameters do not match the ones used in the paper

The Testing Accuracy over MNIST-M [Target Dataset] reaches over ~94% over 100 epochs, as compared to the 76.66% mentioned in the paper.

• Source Accuracy : Self Accuracy Score over MNIST (used for Training)
• Testing Accuracy : Accuracy Score over Testing Set of MNIST-M [Target Dataset]
• Target Accuracy : Accuracy Score over Training Set of MNIST-M [Target Dataset]

References

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., ... & Lempitsky, V. (2016). Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 17(1), 2096-2030.

• http://jmlr.org/papers/volume17/15-239/15-239.pdf
• For more information, see http://sites.skoltech.ru/compvision/projects/grl/