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This is the official implementation for WACV 2021 paper of Unsupervised Multi-Target Domain Adaptation ThroughKnowledge Distillation,

Requirements

Datasets

  • You can download Office31 and OfficeHome from:

      Office-31: https://people.eecs.berkeley.edu/~jhoffman/domainadapt/
  Office Home: http://hemanthdv.org/OfficeHome-Dataset/

  • In order to easily run our code, we expect the datasets for Office31 and OfficeHome to found be at: '~/datasets/', here are how we get the path to our datasets in our code:

      a = os.path.expanduser('~/datasets/amazon/images')
  w = os.path.expanduser('~/datasets/webcam/images')
  d = os.path.expanduser('~/datasets/dslr/images')

  Ar = os.path.expanduser('~/datasets/OfficeHome/Art')
  Cl = os.path.expanduser('~/datasets/OfficeHome/Clipart')
  Pr = os.path.expanduser('~/datasets/OfficeHome/Product')
  Rw = os.path.expanduser('~/datasets/OfficeHome/RealWorld')

Visualization

  • Originally, this project used a mongoObserver from sacred in combination with Omniboard (https://github.com/vivekratnavel/omniboard) to visualize experiments. You can either use this or integrate your own visualizer (e.g. visdom)

Run the code

  • All the code is run using sacred, you can see examples in the folder experiments/

  • Example, to run all of our results on the OfficeHome with AlexNet backbone:

      python experiments/exp_kd_multi_target_da_grl_cst_fac.py

  • For other experiments you can edit exp_kd_multi_target_da_grl_cst_fac.py to change our hyper-parameters

Evaluation

  • Coming soon.

Credits

We used some of the code from: