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Adversarial Reweighting with α-Power Maximization for Partial Domain Adaptation

Code for paper "Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu, Adversarial Reweighting with α-Power Maximization for Domain Adaptation, IJCV, 2024". [Official Version]

This is the extended version of the conference paper "Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu, Adversarial Reweighting with α-Power Maximization for Partial Domain Adaptation. NeurIPS, 2021". The extended version is more stable and more effective.

We have extended our method to open-set domain adaptation (OSDA), universal domain adaptation (UniDA), and test-time adaptation (TTA). Please refer to them.

Prerequisites:

python==3.9.12
torch==2.0.1
torchaudio==2.0.2
torchvision==0.15.2
numpy==1.25.2
cvxpy==1.3.2
tqdm==4.66.1
Pillow==10.0.0
scikit-learn==1.3.0
torch_ema==0.3

Datasets:

Download the datasets of
VisDA-2017
DomainNet
Office-Home
Office
ImageNet
Caltech-256
and put them into the folder "./data/" and modify the path of images in each '.txt' under the folder './data/'. Note the full list of ImageNet (imagenet.txt) is too big. Please download it here and put it into './data/imagenet_caltech/'.

Domain ID:

VisDA-2017: train (synthetic), validation (real) ==> 0,1
DomainNet: clipart, painting, real, sketch ==> 0,1,2,3
Office-Home: art, clipart, product, real_world ==> 0,1,2,3
Office: amazon, dslr, webcam ==> 0,1,2
ImageNet-Caltech: imagenet, caltech ==> 0,1

Training

VisDA-2017:

python main.py --dset visda-2017 --s 0 --t 1

DomainNet:

python main.py --dset domainnet --s 0 --t 1

Office-Home:

python main.py --dset office_home --s 0 --t 1

Office:

python main.py --dset office --s 0 --t 1

ImageNet-Caltech:

python main.py --dset imagenet_caltech --s 0 --t 1

Results

We run the code on a single Tesla V-100 GPU. The results are as follows.

Office:

seed A2D A2W D2A D2W W2A W2D Avg
2019 99.4 99.3 96.7 100.0 96.8 100.0 98.7
2021 100.0 99.7 96.4 100.0 96.6 100.0 98.8
2023 99.4 99.3 96.6 99.7 96.9 100.0 98.7
Avg 99.6 99.4 96.6 99.9 96.8 100.0 98.7

Office-Home:

seed A2C A2P A2R C2A C2P C2R P2A P2C P2R R2A R2C R2P Avg
2019 67.9 90.1 92.8 78.4 81.2 85.0 80.0 67.5 89.5 87.3 72.5 89.2 81.8
2021 67.4 85.7 90.8 76.2 85.8 88.5 81.5 71.0 88.8 85.3 69.2 88.9 81.6
2023 69.5 87.7 93.0 78.8 86.8 85.4 81.7 69.1 90.2 86.1 68.2 89.2 82.1
Avg 68.3 87.8 92.2 77.8 84.6 86.3 81.1 69.2 89.5 86.2 70.0 89.1 81.8

VisDA-2017:

seed 2019 2021 2023 Avg
S2R 92.2 93.9 93.6 93.2

ImageNet-Caltech:

seed I2C C2I Avg
2019 84.3 87.1 85.7
2021 84.6 87.0 85.8
2023 85.0 87.2 86.1
Avg 84.6 87.1 85.9

DomainNet:

seed C2P C2R C2S P2C P2R P2S R2C R2P R2S S2C S2P S2R Avg
2019 65.3 77.0 66.5 78.3 84.3 81.9 86.7 77.9 78.5 66.8 63.8 70.9 74.8
2021 69.1 81.0 65.9 77.4 84.4 82.2 86.5 78.1 77.7 60.1 65.7 72.5 75.1
2023 69.2 81.4 66.6 79.4 83.7 81.5 86.2 78.0 79.6 60.7 65.0 71.8 75.3
Avg 67.9 79.8 66.3 78.4 84.1 81.9 86.5 78.0 78.6 62.5 64.8 71.7 75.0

Reference code:

https://github.com/thuml/CDAN
https://github.com/tim-learn/BA3US
https://github.com/XJTU-XGU/RSDA

Contact：

If you have any problem, feel free to contect xianggu@stu.xjtu.edu.cn.