Awesome

Code for paper Margin-aware Adversarial Domain Adaptation with Optimal Transport

Dependencies:

• Numpy >= 1.18.1
• POT >= 0.6.0
• CVXPY >= 1.0.25
• MOSEK >= 9.1.9
• Scikit-Learn >= 0.22.1

Scripts for experiments:

• Moons:
  • cross validation: cross_valid.py (might take up to 90 minutes to run)
  • testing: postprocessing_cross_valid.py
• Amazon:
  • Data download link
  • testing: postprocessing_cross_valid_amazon.py (with fixed hyperparameters indicated in the main paper)

Scripts for figures

• Moons: postprocessing_cross_valid.py
• Loss function <img src="https://render.githubusercontent.com/render/math?math=l^{\rho, \beta}">: loss_funcs.py
• Smooth proxies (supplementary): proxies.py (end of script, to decomment)

Other scripts:

• Main class for our algorithm: madaot.py
• Cross validation (supports parallelism): myDA.py
• Algorithm computing the transport plan at each step (decribed in Blankenship and Falk, 1976): advEmd.py