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[ECCV 2020] Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation

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Acknowledgment

The implementation is built on the pytorch implementation of SSDA_MME and we refer a specific module in DTA.

Prerequisites

  • CUDA 10.0 or 10.1
  • Python 3.7 (or 3.6)
  • Pytorch 1.0.1
conda install pytorch==1.0.1 torchvision==0.2.2 cudatoolkit=10.0 -c pytorch
  • Pillow, numpy, tqdm
  • You can easily install dependencies through
pip install -r requirements.txt

Dataset Structure

You can download the datasets by following the instructions in SSDA_MME.

data---
     |
   multi---
     |   |
     |  Real
     |  Clipart
     |  Product
     |  Real
   office_home---
     |         |
     |        Art
     |        Clipart
     |        Product
     |        Real
   office---
     |    |
     |   amazon
     |   dslr
     |   webcam
   txt---
       | 
      multi---
       |    |
       |   labeled_source_images_real.txt
       |   unlabeled_target_images_real_3.txt
       |   labeled_target_images_real_3.txt         
       |   unlabeled_source_images_sketch.txt
       |                  ...
      office---
       |     |
       |   labeled_source_images_amazon.txt
       |   unlabeled_target_images_amazon_3.txt
       |   labeled_target_images_amazon_3.txt         
       |   unlabeled_source_images_webcam.txt
       |                  ...
      office_home---
                  |
                 ...       

Example

Train

  • DomainNet (clipart, painting, real, sketch)
python main.py --dataset multi --source real --target sketch --save_interval 5000 --steps 70000 --net resnet34 --num 3 --save_check
  • Office-home (Art, Clipart, Product, Real)
  • Office (amazon, dslr, webcam)

Test

  • DomainNet (clipart, painting, real, sketch)
python test.py --dataset multi --source real --target sketch --steps 70000

Checkpoint samples

Additional Splits

We provide 5, 10, 20-shot splits for four domains (clipart, painting, real, sketch) of the DomainNet dataset.

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[ECCV 2020] Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation

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