Transfer Learning & Domain Adaptation

Transfer Learning vs. Domain Adaptation

Following quoted from Wikipedia

Domain adaptation is the ability to apply an algorithm trained in one or more “source domains” to a different (but related) “target domain”. Domain adaptation is a subcategory of transfer learning. In domain adaptation, the source and target domains all have the same feature space (but different distributions); in contrast, transfer learning includes cases where the target domain’s feature space is different from the source feature space or spaces.

Important Resources

  1. An Overview of Transfer Learning – Video lecture by Sinno Jialin Pan – Presentation by Sinno Jialin Pan et al.
  2. A Survey on Transfer Learning by Sinno Jialin Pan et al. – Paper
  3. Transfer Learning: Repurposing ML Algorithms from different domains to cloud defense – Presentation – 
  4. Transfer Learning by Andrew NgVideo Lecture
  5. An analysis of the transfer learning of convolutional neural networks for artistic images – Paper
  6. Book – Transfer Learning by Qiang Yang et al. –
  7. How transferable are features in deep neural networks? by Yoshua Bengio et al. – paper
  8. Unsupervised Domain Adaptation by Backpropagation by Yaroslav Ganin et al. – paper
  9. Distilling the Knowledge in a Neural Network by Geoffrey Hinton et al. – paper