partial_domain_adaptation

Partial Domain Adaptation for Image Classification

Installation

It’s suggested to use pytorch==1.7.1 and torchvision==0.8.2 in order to reproduce the benchmark results.

Example scripts support all models in PyTorch-Image-Models. You also need to install timm to use PyTorch-Image-Models.

pip install timm

Dataset

Following datasets can be downloaded automatically:

• Office31
• OfficeHome
• VisDA2017

Supported Methods

Supported methods include:

• Domain Adversarial Neural Network (DANN)
• Partial Adversarial Domain Adaptation (PADA)
• Importance Weighted Adversarial Nets (IWAN)
• Adaptive Feature Norm (AFN)

Experiment and Results

The shell files give the script to reproduce the benchmark with specified hyper-parameters. For example, if you want to train DANN on Office31, use the following script

# Train a DANN on Office-31 Amazon -> Webcam task using ResNet 50.
# Assume you have put the datasets under the path `data/office-31`, 
# or you are glad to download the datasets automatically from the Internet to this path
CUDA_VISIBLE_DEVICES=0 python dann.py data/office31 -d Office31 -s A -t W -a resnet50 --epochs 20 --seed 1 --log logs/dann/Office31_A2W

Notations

• Origin means the accuracy reported by the original paper.
• Avg is the accuracy reported by TLlib.
• ERM refers to the model trained with data from the source domain.
• Oracle refers to the model trained with data from the target domain.

We found that the accuracies of adversarial methods (including DANN) are not stable even after the random seed is fixed, thus we repeat running adversarial methods on Office-31 and VisDA-2017 for three times and report their average accuracy.

Office-31 accuracy on ResNet-50

Methods	Origin	Avg	A → W	D → W	W → D	A → D	D → A	W → A
ERM	75.6	90.1	78.3	98.3	99.4	87.3	88.5	88.8
DANN	43.4	82.4	60.0	94.9	98.1	71.3	84.9	85.0
PADA	92.7	93.8	86.4	100.0	100.0	87.3	93.8	95.4
IWAN	94.7	94.8	91.2	99.7	99.4	89.8	94.2	94.3
AFN	/	93.1	87.8	95.6	99.4	87.9	93.9	94.1

Office-Home accuracy on ResNet-50

Methods	Origin	Avg	Ar → Cl	Ar → Pr	Ar → Rw	Cl → Ar	Cl → Pr	Cl → Rw	Pr → Ar	Pr → Cl	Pr → Rw	Rw → Ar	Rw → Cl	Rw → Pr
ERM	53.7	60.1	42.0	66.9	78.5	56.4	55.2	65.4	57.9	36.0	75.5	68.7	43.6	74.8
DANN	47.4	57.0	46.2	59.3	76.9	47.0	47.4	56.4	51.6	38.8	72.1	68.0	46.1	74.2
PADA	62.1	65.9	52.9	69.3	82.8	59.0	57.5	66.4	66.0	41.7	82.5	78.0	50.2	84.1
IWAN	63.6	71.3	59.2	76.6	84.0	67.8	66.7	69.2	73.3	55.0	83.9	79.0	58.3	82.2
AFN	71.8	72.6	59.2	76.7	82.8	72.5	74.5	76.8	72.5	56.7	80.8	77.0	60.5	81.6

VisDA-2017 accuracy on ResNet-50

Methods	Origin	Mean	plane	bcycl	bus	car	horse	knife	Avg
ERM	45.3	50.9	59.2	31.3	68.7	73.2	69.3	3.4	60.0
DANN	51.0	55.9	88.4	34.1	72.1	50.7	61.9	27.8	57.1
PADA	53.5	60.5	89.4	35.1	72.5	69.2	86.7	10.1	66.8
IWAN	/	61.5	89.2	57.0	61.5	55.2	80.1	25.7	66.8
AFN	67.6	61.0	79.1	62.7	73.9	49.6	79.6	21.0	64.1

Citation

If you use these methods in your research, please consider citing.

@inproceedings{DANN,
    author = {Ganin, Yaroslav and Lempitsky, Victor},
    Booktitle = {ICML},
    Title = {Unsupervised domain adaptation by backpropagation},
    Year = {2015}
}

@InProceedings{PADA,
    author    = {Zhangjie Cao and
               Lijia Ma and
               Mingsheng Long and
               Jianmin Wang},
    title     = {Partial Adversarial Domain Adaptation},
    booktitle = {ECCV},
    year = {2018}
}

@InProceedings{IWAN,
    author    = {Jing Zhang and
               Zewei Ding and
               Wanqing Li and
               Philip Ogunbona},
    title     = {Importance Weighted Adversarial Nets for Partial Domain Adaptation},
    booktitle = {CVPR},
    year = {2018}
}

@InProceedings{AFN,
    author = {Xu, Ruijia and Li, Guanbin and Yang, Jihan and Lin, Liang},
    title = {Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation},
    booktitle = {ICCV},
    year = {2019}
}