基于深度神经网络的特征解耦与域适应的迭代式增强方法
Iterative Boosting of Disentangled Synthesis and Domain Adaptation
In this paper, we present a method for boosting Domain Adaptation(DA) performance by leveraging Disentanglement(Di) analysis. The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance.
The method is implemented and tested with Pytorch in python2 and use tensorboard for visualization. Take DSN as backbone and dataset as MNIST-MNISTM as an example：
python train.py --dataroot ./traindata --name DSN_m --model DSN_mv2 --no_dropout --lr 0.01 --lr_de 1 - gpu_ids 0 --lr_policy nopolicy --which_method DSN
run the DA backbone
python train2.py --dataroot ./traindata --name Di_DSN_m --model Di_DSN_m --no_dropout --lr 0.00001 --lr_de 1 - gpu_ids 0 --lr_policy nopolicy --which_method DSN --batchSize 64 --which_epoches_DA [which_epoches_DA] --which_usename_DA DSN_m
run the Di stage
python train3.py --dataroot ./traindata --name DSN_miter1step1 --model DSN_m_iter --no_dropout --lr 0.01 --lr_de 10 - gpu_ids 0 --lr_policy nopolicy --which_method DSN --batchSize 100 --which_epoches_DA [which_epoches_DA] --which_usename_DA DSN_m --which_epoches_Di [which_epoches_Di] --which_usename_Di Di_DSN_m
iteration of DA
python train2.py --dataroot ./traindata --name Di_DSN_iter1m --model Di_iter_DSN_m --no_dropout --lr 0.00001 --lr_de 1 - gpu_ids 0 --lr_policy nopolicy --which_method DSN --batchSize 64 --which_epoches_DA [which_epoches_DA] --which_usename_DA DSN_miter1step1 --which_epoches_Di [which_epoches_Di] --which_usename_Di Di_DSN_m
iteration of Di
use tensorboard for visualization:
cd <your path>/DiDA
tensorboard --logdir=runs --port=6006