Maximum Density Divergence for Domain Adaptation published on IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Authors: Jingjing Li, Erpeng Chen, Zhengming Ding, Lei Zhu, Ke Lu and Heng Tao Shen
PDF on Arxiv: https://arxiv.org/abs/2004.12615 , on IEEE: https://ieeexplore.ieee.org/abstract/document/9080115
- PyTorch >= 1.0.0 (with suitable CUDA and CuDNN version)
- torchvision >= 0.2.2
- Python3
- Numpy
- argparse
- PIL
Please use the following commands for different tasks.
You can find more detailed commands samples in the train.sh file
SVHN->MNIST
python train_svhnmnist.py --mdd_weight 0.01 --epochs 50
USPS->MNIST
python train_uspsmnist.py --mdd_weight 0.01 --epochs 50 --task USPS2MNIST
MNIST->USPS
python train_uspsmnist.py --mdd_weight 0.01 --epochs 50 --task MNIST2USPS
Office-31
python train_image.py --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
python train_image.py --net ResNet50 --dset office-home --test_interval 2000 --s_dset_path ../data/office-home/Art.txt --t_dset_path ../data/office-home/Clipart.txt
Image-clef
python train_image.py --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
The adversarial learning part is inspired by CDAN.