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LAMDA: Label Matching Deep Domain Adaptation

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This is the implementation of paper LAMDA: Label Matching Deep Domain Adaptation which has been accepted at ICML 2021.

A. Setup

A.1. Install Package Dependencies

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Please note that tensorbayes 0.4.0 is out of date.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/

A.2. Datasets

Please download Office-31 here and unzip extracted features in the datasets folder.

B. Training

We first navigate to model folder, and then run run_lamda.py file as bellow:

cd model
  1. 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 ""
  1. 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 ""
  1. 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 ""
  1. 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 ""
  1. 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 ""
  1. 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 ""

C. Results

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

D. References

D.1. Baselines:

[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.

D.2. GitHub repositories:

  • Some parts of our code (e.g., VAT, evaluation, …) are rewritten with modifications from DIRT-T.