This is the Pytorch demo code for Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment (DRMEA) (AAAI 2020)
"DRMEA describes the domains by a sequence of abstract manifolds, and develops a Riemannian manifold learning framework to achieve transferability and discriminability consistently. "
| ImageCLEF | I→P | P→I | I→C | C→I | C→P | P→C | Avg. |
|---|---|---|---|---|---|---|---|
| ResNet-50 | 74.8 ± 0.3 | 83.9 ± 0.1 | 91.5 ± 0.3 | 78.0 ± 0.2 | 65.5 ± 0.3 | 91.2 ± 0.3 | 80.7 |
| DAN | 74.5 ± 0.4 | 82.2 ± 0.2 | 92.8 ± 0.2 | 86.3 ± 0.4 | 69.2 ± 0.4 | 89.8 ± 0.4 | 82.5 |
| DANN | 75.0 ± 0.3 | 86.0 ± 0.3 | 96.2 ± 0.4 | 87.0 ± 0.5 | 74.3 ± 0.5 | 91.5 ± 0.6 | 85.0 |
| JAN | 76.8 ± 0.4 | 88.0 ± 0.2 | 94.7 ± 0.2 | 89.5 ± 0.3 | 74.2 ± 0.3 | 91.7 ± 0.3 | 85.8 |
| CDAN | 76.7 ± 0.3 | 90.6 ± 0.3 | 97.0 ± 0.4 | 90.5 ± 0.4 | 74.5 ± 0.3 | 93.5 ± 0.4 | 87.1 |
| CDAN+E | 77.7 ± 0.3 | 90.7 ± 0.2 | 97.7 ± 0.3 | 91.3 ± 0.3 | 74.2 ± 0.2 | 94.3 ± 0.3 | 87.7 |
| DRMEA (No AL) | 78.0 ± 0.1 | 91.1 ± 0.1 | 95.6 ± 0.2 | 88.7 ± 0.3 | 74.8 ± 0.1 | 94.8 ± 0.2 | 87.3 |
| DRMEA (No DS) | 78.9 ± 0.1 | 90.5 ± 0.2 | 94.0 ± 0.1 | 87.8 ± 0.1 | 76.7 ± 0.2 | 93.0 ± 0.1 | 86.8 |
| DRMEA | 80.7 ± 0.1 | 92.5 ± 0.1 | 97.2 ± 0.1 | 90.5 ± 0.1 | 77.7 ± 0.2 | 96.2 ± 0.2 | 89.1 |
- python 3.6
- PyTorch 1.0
-
The dataset should be placed in
./Dataset, e.g.,./Dataset/ImageCLEF -
The structure of the datasets should be like
Image-CLEF (Dataset)
|- I (Domain)
| |- aeroplane (Class)
| |- XXXX.jpg (Sample)
| |- ...
| |- bike (Class)
| |- ...
|- P (Domain)
|- C (Domain)
-
Download the
Image-CLEFdataset from Google Drive -
Training with config
python main.py --dset ImageCLEF --mEpo 50 --ExpTime 10 --BatchSize 32 -
Experiment results refer to Variables:
ACC_Recorder and Total_Result
-
Best model and epxerimental logs can be found in folder
./Model_Log/...
If this repository is helpful for you, please cite our paper:
@inproceedings{luo2020unsupervised,
title={Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment},
author={You-Wei Luo, and Chuan-Xian Ren, and Pengfei Ge, and Ke-kun Huang, and Yu-Feng Yu},
booktitle={AAAI},
year={2020}
}
If you have any questions, please feel free contact me via [email protected].
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