Implementation of "Generate To Adapt: Aligning Domains using Generative Adversarial Networks" in PyTorch

Please download the dataset from https://www.cs.umd.edu/~yogesh/datasets/digits.zip and extract it. This folder contains the dataset in the same format as need by our code.

Let us train the Lenet model for SVHN->MNIST Domain adaptation. Obtain the baseline numbers by running

python main.py --dataroot [path to the dataset] --method sourceonly

To train our method(GTA), run

python main.py --dataroot [path to the dataset] --method GTA

This code trains and stores the trained models in result folder. Current checkpoint and the model that gives best performance on the validation set are stored.

To evaluate the trained models on the target domain (MNIST), run

python eval.py --dataroot [path to the dataset] --method GTA --model_best False

If you use this code for your research, please cite

@article{Gen2Adapt,
    author    = {Swami Sankaranarayanan and
           Yogesh Balaji and
           Carlos D. Castillo and
           Rama Chellappa},
    title     = {Generate To Adapt: Aligning Domains using Generative Adversarial Networks},
    journal   = {CoRR},
    volume    = {abs/1704.01705},
    year      = {2017},
    url       = {https://arxiv.org/abs/1704.01705},
}