Generate_To_Adapt

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Generate_To_Adapt

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

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eval.py
main.py
models.py
README.md
trainer.py
utils.py

// repository documentation

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

## Datasets:

Please download the dataset from http://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.

## Training:

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.

## Evaluation:

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

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

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       = {http://arxiv.org/abs/1704.01705},
    }

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