This is the implementation of paper LAMDA: Label Matching Deep Domain Adaptation which has been accepted at ICML 2021.
Install manually
Python Environment: >= 3.5
Tensorflow: >= 1.9
Install automatically from YAML file
pip install --upgrade pip
conda env create --file tf1.9py3.5.yml
[UPDATE] Install tensorbayes
Please note that tensorbayes 0.4.0 is out of date. Please copy a newer version to the env folder (tf1.9py3.5) using tensorbayes.tar
source activate tf1.9py3.5
pip install tensorbayes
tar -xvf tensorbayes.tar
cp -rf /tensorbayes/* /opt/conda/envs/tf1.9py3.5/lib/python3.5/site-packages/tensorbayes/
Please download Office-31 here and unzip extracted features in the datasets folder.
We first navigate to model folder, and then run run_lamda.py file as bellow:
cd model- A --> W task
python run_lamda.py 1 amazon webcam format csv num_iters 20000 summary_freq 400 learning_rate 0.0001 inorm True batch_size 310 src_class_trade_off 1.0 domain_trade_off 0.1 src_vat_trade_off 0.1 trg_trade_off 0.1 save_grads False cast_data False cnn_size small update_target_loss False m_on_D_trade_off 1.0 m_plus_1_on_D_trade_off 1.0 m_plus_1_on_G_trade_off 1.0 m_on_G_trade_off 0.1 data_path ""- A --> D task
python run_lamda.py 1 amazon dslr format csv num_iters 20000 summary_freq 400 learning_rate 0.0001 inorm True batch_size 310 src_class_trade_off 1.0 domain_trade_off 0.1 src_vat_trade_off 1.0 trg_trade_off 0.1 save_grads False cast_data False cnn_size small update_target_loss False m_on_D_trade_off 1.0 m_plus_1_on_D_trade_off 1.0 m_plus_1_on_G_trade_off 1.0 m_on_G_trade_off 0.05 data_path ""- D --> W task
python run_lamda.py 1 dslr webcam format csv num_iters 20000 summary_freq 400 learning_rate 0.0001 inorm True batch_size 155 src_class_trade_off 1.0 domain_trade_off 0.1 src_vat_trade_off 0.1 trg_trade_off 0.1 save_grads False cast_data False cnn_size small update_target_loss False m_on_D_trade_off 1.0 m_plus_1_on_D_trade_off 1.0 m_plus_1_on_G_trade_off 1.0 m_on_G_trade_off 0.1 data_path ""- W --> D task
python run_lamda.py 1 webcam dslr format csv num_iters 20000 summary_freq 400 learning_rate 0.0001 inorm True batch_size 310 src_class_trade_off 1.0 domain_trade_off 0.1 src_vat_trade_off 0.1 trg_trade_off 0.1 save_grads False cast_data False cnn_size small update_target_loss False m_on_D_trade_off 1.0 m_plus_1_on_D_trade_off 1.0 m_plus_1_on_G_trade_off 1.0 m_on_G_trade_off 0.1 data_path ""- D --> A task
python run_lamda.py 1 dslr amazon format csv num_iters 20000 sumary_freq 400 learning_rate 0.0001 inorm True batch_size 155 src_class_trade_off 1.0 domain_trade_off 0.1 src_vat_trade_off 1.0 trg_trade_off 0.1 save_grads False cast_data False cnn_size small update_target_loss False m_on_D_trade_off 1.0 m_plus_1_on_D_trade_off 1.0 m_plus_1_on_G_trade_off 1.0 m_on_G_trade_off 1.0 data_path ""- W --> A task
python run_lamda.py 1 webcam amazon format csv num_iters 20000 summary_freq 400 learning_rate 0.0001 inorm True batch_size 310 src_class_trade_off 1.0 domain_trade_off 0.1 src_vat_trade_off 1.0 trg_trade_off 0.1 save_grads False cast_data False cnn_size small update_target_loss False m_on_D_trade_off 1.0 m_plus_1_on_D_trade_off 1.0 m_plus_1_on_G_trade_off 1.0 m_on_G_trade_off 1.0 data_path ""| Methods | A --> W | A --> D | D --> W | W --> D | D --> A | W --> A | Avg |
|---|---|---|---|---|---|---|---|
| ResNet-50 [1] | 70.0 | 65.5 | 96.1 | 99.3 | 62.8 | 60.5 | 75.7 |
| DeepCORAL [2] | 83.0 | 71.5 | 97.9 | 98.0 | 63.7 | 64.5 | 79.8 |
| DANN [3] | 81.5 | 74.3 | 97.1 | 99.6 | 65.5 | 63.2 | 80.2 |
| ADDA [4] | 86.2 | 78.8 | 96.8 | 99.1 | 69.5 | 68.5 | 83.2 |
| CDAN [5] | 94.1 | 92.9 | 98.6 | 100.0 | 71.0 | 69.3 | 87.7 |
| TPN [6] | 91.2 | 89.9 | 97.7 | 99.5 | 70.5 | 73.5 | 87.1 |
| DeepJDOT [7] | 88.9 | 88.2 | 98.5 | 99.6 | 72.1 | 70.1 | 86.2 |
| RWOT [8] | 95.1 | 94.5 | 99.5 | 100.0 | 77.5 | 77.9 | 90.8 |
| LAMDA | 95.2 | 96.0 | 98.5 | 99.8 | 87.3 | 84.4 | 93.0 |
[1] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016.
[2] B. Sun and K. Saenko. Deep coral: Correlation alignment for deep domain adaptation. In Gang Hua and Hervé Jéegou, editors, Computer Vision – ECCV 2016 Workshops, pages 443–450, Cham, 2016. Springer International Publishing.
[3] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky. Domain-adversarial training of neural networks. J. Mach. Learn. Res., 17(1):2096–2030, jan 2016.
[4] E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell. Adversarial discriminative domain adaptation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2962–2971, 2017.
[5] M. Long, Z. Cao, J. Wang, and M. I. Jordan. Conditional adversarial domain adaptation. In Advances in Neural Information Processing Systems 31, pages 1640–1650. Curran Associates, Inc., 2018.
[6] Y. Pan, T. Yao, Y. Li, Y. Wang, C. Ngo, and T. Mei. Transferrable prototypical networks for unsupervised domain adaptation. In CVPR, pages 2234–2242, 2019.
[7] B. B. Damodaran, B. Kellenberger, R. Flamary, D. Tuia, and N. Courty. Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation. In Computer Vision - ECCV 2018, pages 467–483. Springer, 2018.
[8] R. Xu, P. Liu, L. Wang, C. Chen, and J. Wang. Reliable weighted optimal transport for unsupervised domain adaptation. In CVPR 2020, June 2020.
- Some parts of our code (e.g., VAT, evaluation, …) are rewritten with modifications from DIRT-T.