Domain Adaptation

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Methods

  1. learn projection matrix: F(PXs, QXt)

  2. sample selection: learn sample weights

  3. domain-invariant and domain-specific components

  4. low-rank reconstruction

  5. pixel-level image to image translation

    • paired input: conditional GAN [pdf]
    • unpaired input: cycling GAN [pdf], GAN with content-similarity loss [pdf], UNIT [pdf]
    • combine with feature-based method: GraspGAN [pdf]
    • A unified framework [pdf]

  6. adversarial network [1]: classification and domain confusion. The domain separation and confusion problem, which is a min-max problem, can be solved like GAN or using reverse gradient (RevGrad) algorithm.

  7. meta-learning

    • gradients on two domains should be consistent [pdf]

  8. domain alignment layer (batch normalization): [1] [2]

  9. guided learning: tutor guides students and get feedback from students. ACM-MM18 paper

  10. ensemble transfer learning: aggregate multiple transfer learning approaches [1]

Settings

  1. open-set domain adaptation or partial transfer learning: [1][2][3]

  2. distant domain adaptation (two domains are too distant, so the transfer between them relies on transition domains): Transitive transfer learning, distant domain transfer learning

  3. open compound domain adaptation [1]

Domain adaptation for diverse applications

  1. pose estimation [1]

  2. person re-identification [1]

  3. objection detection [1]

  4. segmentation [1]

  5. VQA [1]

Domain difference metric: To measure data distribution mismatch, the most commonly used metric is MMD and its extensions such as fast MMD, conditional MMD [1][2] and joint MMD. There are also some other metrics like KL divergence, HSIC criterion, Bregman divergence, manifold criterion, and second-order statistic.

Theories: A summary of related theories

Survey:

  1. An old survey of transfer learning [pdf]
  2. Recent advance on domain adaptation [pdf]
  3. My survey of old deep learning domain adaptation methods [pdf]
  4. A Chinese version of transfer learning tutorial [pdf]
  5. Datasets and code: [1]
  6. A Comprehensive Survey on Transfer Learning [pdf]