Deep Transfer Learning in PyTorch

This is a PyTorch library for deep transfer learning. We divide the code into two aspects: Single-source Unsupervised Domain Adaptation (SUDA) and Multi-source Unsupervised Domain Adaptation (MUDA). There are many SUDA methods, however I find there is a few MUDA methods with deep learning. Besides, MUDA with deep learning might be a more promising direction for domain adaptation.

Here I have implemented some deep transfer methods as follows:

• UDA
  • DDC：Deep Domain Confusion Maximizing for Domain Invariance
  • DAN: Learning Transferable Features with Deep Adaptation Networks (ICML2015)
  • Deep Coral: Deep CORAL Correlation Alignment for Deep Domain Adaptation (ECCV2016)
  • Revgrad: Unsupervised Domain Adaptation by Backpropagation (ICML2015)
  • MRAN: Multi-representation adaptation network for cross-domain image classification (Neural Network 2019)
  • DSAN: Deep Subdomain Adaptation Network for Image Classification (IEEE Transactions on Neural Networks and Learning Systems 2020)
• MUDA
  • Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources (AAAI2019)
• Application
  • Cross-domain Fraud Detection: Modeling Users’ Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection (WWW2020)
  • Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising (KDD2021)
• Survey
  • A Comprehensive Survey on Transfer Learning (Proc. IEEE)

Results on Office31(UDA)

Method	A - W	D - W	W - D	A - D	D - A	W - A	Average
ResNet	68.4±0.5	96.7±0.5	99.3±0.1	68.9±0.2	62.5±0.3	60.7±0.3	76.1
DDC	75.8±0.2	95.0±0.2	98.2±0.1	77.5±0.3	67.4±0.4	64.0±0.5	79.7
DDC*	78.3±0.4	97.1±0.1	100.0±0.0	81.7±0.9	65.2±0.6	65.1±0.4	81.2
DAN	83.8±0.4	96.8±0.2	99.5±0.1	78.4±0.2	66.7±0.3	62.7±0.2	81.3
DAN*	82.6±0.7	97.7±0.1	100.0±0.0	83.1±0.9	66.8±0.3	66.6±0.4	82.8
DCORAL*	79.0±0.5	98.0±0.2	100.0±0.0	82.7±0.1	65.3±0.3	64.5±0.3	81.6
Revgrad	82.0±0.4	96.9±0.2	99.1±0.1	79.7±0.4	68.2±0.4	67.4±0.5	82.2
Revgrad*	82.6±0.9	97.8±0.2	100.0±0.0	83.3±0.9	66.8±0.1	66.1±0.5	82.8
MRAN	91.4±0.1	96.9±0.3	99.8±0.2	86.4±0.6	68.3±0.5	70.9±0.6	85.6
DSAN	93.6±0.2	98.4±0.1	100.0±0.0	90.2±0.7	73.5±0.5	74.8±0.4	88.4

Note that the results without '*' comes from paper. The results with '*' are run by myself with the code.

Results on Office31(MUDA)

Standards	Method	A,W - D	A,D - W	D,W - A	Average
	ResNet	99.3	96.7	62.5	86.2
	DAN	99.5	96.8	66.7	87.7
Single Best	DCORAL	99.7	98.0	65.3	87.7
	RevGrad	99.1	96.9	68.2	88.1
	DAN	99.6	97.8	67.6	88.3
Source Combine	DCORAL	99.3	98.0	67.1	88.1
	RevGrad	99.7	98.1	67.6	88.5
Multi-Source	MFSAN	99.5	98.5	72.7	90.2

Results on OfficeHome(MUDA)

Standards	Method	C,P,R - A	A,P,R - C	A,C,R - P	A,C,P - R	Average
	ResNet	65.3	49.6	79.7	75.4	67.5
	DAN	64.1	50.8	78.2	75.0	67.0
Single Best	DCORAL	68.2	56.5	80.3	75.9	70.2
	RevGrad	67.9	55.9	80.4	75.8	70.0
	DAN	68.5	59.4	79.0	82.5	72.4
Source Combine	DCORAL	68.1	58.6	79.5	82.7	72.2
	RevGrad	68.4	59.1	79.5	82.7	72.4
Multi-Source	MFSAN	72.1	62.0	80.3	81.8	74.1

Note that (1) Source combine: all source domains are combined together into a traditional single-source v.s. target setting. (2) Single best: among the multiple source domains, we report the best single source transfer results. (3) Multi-source: the results of MUDA methods.

Note

If you find that your accuracy is 100%, the problem might be the dataset folder. Please note that the folder structure required for the data provider to work is:

-dataset
    -amazon
    -webcam
    -dslr

Contact

If you have any problem about this library, please create an Issue or send us an Email at:

• zhuyongchun18s@ict.ac.cn
• jindongwang@outlook.com

Reference

If you use this repository, please cite the following papers:

@inproceedings{zhu2019aligning,
  title={Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources},
  author={Zhu, Yongchun and Zhuang, Fuzhen and Wang, Deqing},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={5989--5996},
  year={2019}
}

@article{zhu2020deep,
  title={Deep subdomain adaptation network for image classification},
  author={Zhu, Yongchun and Zhuang, Fuzhen and Wang, Jindong and Ke, Guolin and Chen, Jingwu and Bian, Jiang and Xiong, Hui and He, Qing},
  journal={IEEE transactions on neural networks and learning systems},
  volume={32},
  number={4},
  pages={1713--1722},
  year={2020},
  publisher={IEEE}
}

@article{zhu2019multi,
  title={Multi-representation adaptation network for cross-domain image classification},
  author={Zhu, Yongchun and Zhuang, Fuzhen and Wang, Jindong and Chen, Jingwu and Shi, Zhiping and Wu, Wenjuan and He, Qing},
  journal={Neural Networks},
  volume={119},
  pages={214--221},
  year={2019},
  publisher={Elsevier}
}