Complicateddd / R-DFDN

A More Robust Domain Feature Decoupling Network. <Huawei 2020 2nd Artificial Intelligence Innovation Competition>

Introduction

This is a pytorch repository of method ‘A more robust domain feature decoupling network ’(R-DFDN), which is heavily followed by https://github.com/salesforce/corr_based_prediction。

Main contributer：Zechen. Zhao 、Shijie.Li 、Tian.Tian

Our model is more robust and stable compared with Corr-Prediction：

Usage && Implement

Requirement：

Python3.6

Pytorch1.2

Numpy1.8

Tqdm

Run：

1、Generate Colored MNIST:

python gen_color_mnist.py

2、Run Correlation based regularization:

python main.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --save_dir corr --beta 0.1

3、Run existing regularization methods:

Maximum Likelihood Estimate (MLE):

python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.0001 --bs 128 --save_dir mle

Adaptive Batch Normalization (AdaBN):

python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.0001 --bs 32 --save_dir adabn --bn --bn_eval

Clean Logit Pairing (CLP):

python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.0001 --save_dir clp --clp --beta 0.5

Projected Gradient Descent (PGD) based adversarial training:

python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.0001 --save_dir pgd --pgd --nsteps 20 --stepsz 2 --epsilon 8

Variational Information Bottleneck (VIB):

python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.001 --save_dir inp --inp_noise 0.2

4、Run our R-DFDN：

Commands to run R-DFDN:

python train_RDFDN.py --use tf_board True --epochs 300 

See the result in tensorboardX:

5、Run ADDA \ RevGrad \ WDGRL:

The same as a implement in https://github.com/jvanvugt/pytorch-domain-adaptation

Some result:

Evaluation on best model forom c-mnist

	CMNIST(B)	MNIST	MNIST-M	SVHN	Absolute Gain
Corr-Prediction	96.88	85.94	79.69	43.75	+0.00
MLE	11.72	7.81	8.59	11.72	\
ADABN	12.5	16.41	12.5	9.38	\
CLP	22.66	7.81	9.38	13.28	\
PGD	18.75	13.28	8.59	7.03	\
VIB	16.41	13.28	11.72	11.72	\
R-DFDN(ours)	96.09	97.65	85.16	60.94	\
	-0.78	+11.71	+5.47	+17.19	+33.58