domain_adaptation.yaml

-
  model:       Multi-task tri-training
  authors:     Ruder and Plank
  year:        2018
  DVD:         78.14
  Books:       74.86
  Electronics: 81.45
  Kitchen:     82.14
  Average:     79.15
  paper:       Strong Baselines for Neural Semi-supervised Learning under Domain Shift
  url:         https://arxiv.org/abs/1804.09530
  code:        []
-
  model:       Asymmetric tri-training
  authors:     Saito et al.
  year:        2017
  DVD:         76.17
  Books:       72.97
  Electronics: 80.47
  Kitchen:     83.97
  Average:     78.39
  paper:       Asymmetric Tri-training for Unsupervised Domain Adaptation
  url:         https://arxiv.org/abs/1702.08400
  code:        []
-
  model:       VFAE
  authors:     Louizos et al.
  year:        2015
  DVD:         76.57
  Books:       73.4
  Electronics: 80.53
  Kitchen:     82.93
  Average:     78.36
  paper:       The Variational Fair Autoencoder
  url:         https://arxiv.org/abs/1511.00830
  code:        []
-
  model:       DANN
  authors:     Ganin et al.
  year:        2016
  DVD:         75.4
  Books:       71.43
  Electronics: 77.67
  Kitchen:     80.53
  Average:     76.26
  paper:       Domain-Adversarial Training of Neural Networks
  url:         https://arxiv.org/abs/1505.07818
  code:        []