chainer-PixelDA

This is an unofficial chainer re-implementation of a paper, Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks [Bousmalis+, CVPR2017].

Requirements

• Python 3.5+
• Chainer 2.0+
• Numpy
• Matplotlib

Performance on MNIST -> MNIST-M

Note that this is not reproduced perfectly. Batchsize=32, #epoch=200.

Method	Original [1]	Ours
Source-only	63.6 %	59.5 %
Target-only	96.4 %	95.9 %
PixelDA	98.2 %	98.0 %

Usage

Preprocess

Download MNIST-M dataset from here (in .pkl format).

Training source-only model (training on MNIST, test on MNIST-M)

python train.py source_only --gpu gpuno --out directory_out

Training target-only model (training on MNIST-M, test on MNIST-M)

python train.py target_only --gpu gpuno --out directory_out

Training PixelDA model (training on MNIST-M, test on MNIST-M)

python train_gan.py --gpu gpuno --out directory_out

Loss curve	Accuracy

References

• [1]: K. Bousmalis, et al. "Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks.", in CVPR, 2017.
• [2]: Original implementation