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https://github.com/easezyc/deep-transfer-learning
A collection of implementations of deep domain adaptation algorithms
https://github.com/easezyc/deep-transfer-learning
deep-transfer-learning domain-adaptation pytorch transfer-learning
Last synced: about 2 months ago
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A collection of implementations of deep domain adaptation algorithms
- Host: GitHub
- URL: https://github.com/easezyc/deep-transfer-learning
- Owner: easezyc
- License: mit
- Created: 2018-07-06T08:06:17.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-03-03T07:30:28.000Z (almost 3 years ago)
- Last Synced: 2024-08-03T23:24:10.320Z (5 months ago)
- Topics: deep-transfer-learning, domain-adaptation, pytorch, transfer-learning
- Language: Python
- Homepage:
- Size: 6.99 MB
- Stars: 868
- Watchers: 9
- Forks: 203
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-machine-learning-resources - **[Code Collection - transfer-learning?style=social) (Table of Contents)
README
# Deep Transfer Learning in PyTorch
[](https://opensource.org/licenses/MIT)
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](https://arxiv.org/abs/1911.02685) (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](http://ise.thss.tsinghua.edu.cn/~mlong/doc/multi-adversarial-domain-adaptation-aaai18.pdf). 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:
* [email protected]
* [email protected]
## 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}
}
```