Code release for "Transferable Normalization: Towards Improving Transferability of Deep Neural Networks" (NeurIPS 2019)
- PyTorch >= 0.4.0 (with suitable CUDA and CuDNN version)
- torchvision >= 0.2.1
- Python3
- Numpy
- argparse
- PIL
Office-31
pythonn train_image.py --gpu_id id --net ResNet50 --dset office --test_interval 500 --s_dset_path ../data/office/amazon_list.txt --t_dset_path ../data/office/webcam_list.txt
Office-Home
pythonn train_image.py --gpu_id id --net ResNet50 --dset office-home --test_interval 2000 --s_dset_path ../data/office-home/Art.txt --t_dset_path ../data/office-home/Clipart.txt
VisDA 2017
pythonn train_image.py --gpu_id id --net ResNet50 --dset visda --test_interval 5000 --s_dset_path ../data/visda-2017/train_list.txt --t_dset_path ../data/visda-2017/validation_list.txt
Image-clef
pythonn train_image.py --gpu_id id --net ResNet50 --dset image-clef --test_interval 500 --s_dset_path ../data/image-clef/b_list.txt --t_dset_path ../data/image-clef/i_list.txt
This code is implemented based on the published code of CDAN and BatchNorm, and it is our pleasure to acknowledge their contributions. CDAN (Conditional Adversarial Domain Adaptation) BatchNorm (Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift)
If you use this code for your research, please consider citing:
@inproceedings{Wang19TransNorm,
title = {Transferable Normalization: Towards Improving Transferability of Deep Neural Networks},
author = {Wang, Ximei and Jin, Ying and Long, Mingsheng and Wang, Jianmin and Jordan, Michael I},
booktitle = {Advances in Neural Information Processing Systems 32},
year = {2019}
}
If you have any problem about our code, feel free to contact
or describe your problem in Issues.