fazil
/
pytorch-dann-resnet
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
A PyTorch implementation for paper Unsupervised Domain Adaptation by Backpropagation
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
5 Commits
1 Branch
0 Tags
152 KiB
 
Tree: 678d117836
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '678d117836'
${ noResults }
wogong 678d117836
update gitignore. 		7 years ago
core add office datasets and experiment. 7 years ago
datasets add office datasets and experiment. 7 years ago
models add office datasets and experiment. 7 years ago
.gitignore update gitignore. 7 years ago
LICENSE Initial commit 7 years ago
README.md initial commit 7 years ago
main.py minor update. 7 years ago
main_office.py add office datasets and experiment. 7 years ago
params.py add office datasets and experiment. 7 years ago
utils.py minor update. 7 years ago

README.md

PyTorch-DANN

A pytorch implementation for paper Unsupervised Domain Adaptation by Backpropagation

InProceedings (icml2015-ganin15)
Ganin, Y. & Lempitsky, V.
Unsupervised Domain Adaptation by Backpropagation
Proceedings of the 32nd International Conference on Machine Learning, 2015

Environment

  • Python 2.7
  • PyTorch 0.3.1

Result

results of the default params.py

MNIST (Source) USPS (Target)
Source Classifier 99.140000% 83.978495%
DANN 97.634409%

Credit